Video Aesthetic Quality Assessment by Temporal Integration of Photo- and Motion-Based Features. Wei-Ta Chu
|
|
- Joan Lewis
- 5 years ago
- Views:
Transcription
1 1 Video Aesthetic Quality Assessment by Temporal Integration of Photo- and Motion-Based Features Wei-Ta Chu H.-H. Yeh, C.-Y. Yang, M.-S. Lee, and C.-S. Chen, Video Aesthetic Quality Assessment by Temporal Integration of Photo- and Motion-Based Features, IEEE Transactions on Multimedia, vol. 15, no. 8, pp , 2013.
2 Introduction 2 Six factors for visual aesthetic impression: color, form (shape), motion, spatial layout, depth, and human body. Goal: design an automatic VAQ assessor by integrating the aesthetic features in the temporal domain.
3 Overview of the Approach 3 Let be a set of videos, where each video is associated with a label representing its aesthetic value, say, 1 for appealing and 0 for unappealing, respectively. A video is separated into several nonoverlapping snippets and each snippet contains consecutive frames in one second. Two stage approach Snippet-based features (SBF) Temporal integration to combine the SBFs Perform a binary classification by SVM
4 4 Preprocessing for Motion-Related Features Suppose each video is divided into snippets, and each snippet is composed of frames, Motion-extraction preprocessing. Use the optical flow techniques to do pixel-based motion estimation. Saliency-based preprocessing. Use a video-based saliency region detection method.
5 5 Preprocessing for Motion-Related Features We binarize the video-based saliency map obtained for each frame, and then extract the largest connected component of the binarized map and refer it to as the foreground region. Motion-related features Motion space Motion direction entropy Hand-shaking Shooting type
6 Motion-Related Features 6 Motion Space (MS). As cameramen are videotaping a moving subject, they must beware that the space in front of the moving direction of the subject should be reserved for better VAQ. Setting the direction of the averaged optical flow of the entire image as v and the vector between the foreground center and image center as d. The feature of MS is
7 Motion-Related Features 7 Motion Direction Entropy (MDE). Compute the velocity directions of pixels and votes them into five predefined bins (upward, rightward, downward, leftward, and quasi-steady). A motion histogram is formed as where
8 Motion-Related Features 8 Hand shaking (HS). We set shaking detection area at the border which can better distinguish subject s self-shaking from the hand-shaking. The horizontal motion of the border region in frame j is defined as, which is the average of the horizontal projections of the optical flow in the region, and its direction is. The horizontal unstableness feature is defined as Similarly, vertical unstableness
9 Motion-Related Features 9 Shooting Type (ST). Define the border to central unstableness ratio for horizontal motion, where is the horizontal motion of the frame border, and is the horizontal motion of the frame except for the border. For border moving faster than the centre type, it may be the recording type that the shot traces and focuses on the subject. For border moving equally with the centre type, it may be a panorama shooting. For border moving slower than the centre type, it may be regarded as a static shot on a deformable subject.
10 Adoption of Photo-Based Features 10 Color harmonization Color saturation and value Composition Lightness
11 Snippet-Based Features 11 The frame-based features are pooled for a video snippet Six pooling operations: = {min, 1 st quartile, mean, median, 3 rd quartile, max} Combining these six pooling operators with the 22 frame-based features, there are thus a total of 132 SBFs allowed to be selected. For example, the SBF of (f 2, min) means that the snippet feature is built by extracting f 2 (the motion direction entropy) for each frame at first, and then pooled by min (i.e., taking the minimum). We denote the Cartesian product
12 Temporal-Variation-Aware Integration 12 Change of SBFs with time will affect the visual preference for audiences. A temporal-variation-aware (TVA) method to integrate the SBFs. Divide the video into non-overlapping segments and propose to use the correlation between segments to perform the integration. Let the k snippets in a video be divided into m segments (groups of snippets); each segment contains d successive snippets, and so k=md. Each group can be characterized by a d-dimensional feature vector if the snippet feature type has been selected in.
13 Temporal-Variation-Aware Integration 13 When the type of SBF is given, there are m feature vectors,, where. Let be the kernel function that measures the similarity in some implicit mapping space. To reflect the ith group s relevance By doing so, we obtain m integration operators To construct the kernel where is the sign function.
14 Temporal-Variation-Aware Integration 14 We include further the (m+1)th and (m+2)th operators which are respectively the mean and standard deviation, denoted as and We have conducted three versions of the proposed methods for comparison.
15 Selection of Key Dimensions 15 The first version, AFSim, selects the features from, i.e., it integrates the SBFs by using only the simple average operators, mean and standard deviation. The second version, AFTVA, selects the features from i.e., it uses the TVA operators to integrate the SBFs. The third version, TVASim, selects the features from i.e., all operators are allowed to be used for temporal integration. The combination of SBFs and temporal integration is very high-dimensional. We employ the sequential forward-search method that is a greedy approach to pick the combination of SBF and integration-operator in turn.
16 Selection of Key Dimensions 16 The procedure of selection 1. Find a SBF-operator pair that best classifies all videos 2. Pick another SBF-operator pair, which ahs the best accuracy jointed with the previously selected pairs. 3. Repeat Step 2 until the number of selections reaches a predefined number Combining the SBF of (f 1, mean) with the temporal integration operator means that we build the video-based feature by extracting f 1 for each frame at first, pooling the features for frames in a snippet by mean to obtain the SBF, and then perform the temporal operator to integrate the values of all SBFs in a video.
17 Experimental Results 17 Telefonica dataset: 160 rated videos. Each video is cropped as a 15-seconds clip. 33 participants rated the videos. The rating values range between [-2,2]. We sort these MOSs and choose the median value as a threshold to separate these 160 videos into positive (high quality) and negative (low quality)
18 Experimental Results 18 Comparisons of designed features. 5-fold cross-validation and repeat it 200 times to obtain the averaged accuracy. The assessment accuracies are by using AFSim and by using MoorthySim. The motion-based features such as ST and HS have been chosen in the first three runs.
19 Experimental Results 19 Comparisons of temporal integrations. AFTVA and TVASim perform better than AFSim and MoorthySim. When the number of selection is increased, AFTVA can further boost the performance by keeping finding more discriminating SBFs. AFTVA can turn more video-based features into distinctive clues.
20 Experimental Results 20 By considering both TVA and the averaged long-term operators (i.e., mean, std), TVASim achieves even-better performance. The top ranked feature types in TVASim are ST, MS, and HS, showing again that motion-related features are highly effective in determining VAQ.
Short Survey on Static Hand Gesture Recognition
Short Survey on Static Hand Gesture Recognition Huu-Hung Huynh University of Science and Technology The University of Danang, Vietnam Duc-Hoang Vo University of Science and Technology The University of
More informationClassification of Protein Crystallization Imagery
Classification of Protein Crystallization Imagery Xiaoqing Zhu, Shaohua Sun, Samuel Cheng Stanford University Marshall Bern Palo Alto Research Center September 2004, EMBC 04 Outline Background X-ray crystallography
More informationShort Run length Descriptor for Image Retrieval
CHAPTER -6 Short Run length Descriptor for Image Retrieval 6.1 Introduction In the recent years, growth of multimedia information from various sources has increased many folds. This has created the demand
More informationVideo Inter-frame Forgery Identification Based on Optical Flow Consistency
Sensors & Transducers 24 by IFSA Publishing, S. L. http://www.sensorsportal.com Video Inter-frame Forgery Identification Based on Optical Flow Consistency Qi Wang, Zhaohong Li, Zhenzhen Zhang, Qinglong
More informationRobustness of Selective Desensitization Perceptron Against Irrelevant and Partially Relevant Features in Pattern Classification
Robustness of Selective Desensitization Perceptron Against Irrelevant and Partially Relevant Features in Pattern Classification Tomohiro Tanno, Kazumasa Horie, Jun Izawa, and Masahiko Morita University
More informationHuman Motion Detection and Tracking for Video Surveillance
Human Motion Detection and Tracking for Video Surveillance Prithviraj Banerjee and Somnath Sengupta Department of Electronics and Electrical Communication Engineering Indian Institute of Technology, Kharagpur,
More informationCORRELATION BASED CAR NUMBER PLATE EXTRACTION SYSTEM
CORRELATION BASED CAR NUMBER PLATE EXTRACTION SYSTEM 1 PHYO THET KHIN, 2 LAI LAI WIN KYI 1,2 Department of Information Technology, Mandalay Technological University The Republic of the Union of Myanmar
More informationClassification of objects from Video Data (Group 30)
Classification of objects from Video Data (Group 30) Sheallika Singh 12665 Vibhuti Mahajan 12792 Aahitagni Mukherjee 12001 M Arvind 12385 1 Motivation Video surveillance has been employed for a long time
More informationP-CNN: Pose-based CNN Features for Action Recognition. Iman Rezazadeh
P-CNN: Pose-based CNN Features for Action Recognition Iman Rezazadeh Introduction automatic understanding of dynamic scenes strong variations of people and scenes in motion and appearance Fine-grained
More informationAdaptive Learning of an Accurate Skin-Color Model
Adaptive Learning of an Accurate Skin-Color Model Q. Zhu K.T. Cheng C. T. Wu Y. L. Wu Electrical & Computer Engineering University of California, Santa Barbara Presented by: H.T Wang Outline Generic Skin
More informationSpatial Information Based Image Classification Using Support Vector Machine
Spatial Information Based Image Classification Using Support Vector Machine P.Jeevitha, Dr. P. Ganesh Kumar PG Scholar, Dept of IT, Regional Centre of Anna University, Coimbatore, India. Assistant Professor,
More informationComment Extraction from Blog Posts and Its Applications to Opinion Mining
Comment Extraction from Blog Posts and Its Applications to Opinion Mining Huan-An Kao, Hsin-Hsi Chen Department of Computer Science and Information Engineering National Taiwan University, Taipei, Taiwan
More informationMULTI ORIENTATION PERFORMANCE OF FEATURE EXTRACTION FOR HUMAN HEAD RECOGNITION
MULTI ORIENTATION PERFORMANCE OF FEATURE EXTRACTION FOR HUMAN HEAD RECOGNITION Panca Mudjirahardjo, Rahmadwati, Nanang Sulistiyanto and R. Arief Setyawan Department of Electrical Engineering, Faculty of
More informationPerceptual Quality Improvement of Stereoscopic Images
Perceptual Quality Improvement of Stereoscopic Images Jong In Gil and Manbae Kim Dept. of Computer and Communications Engineering Kangwon National University Chunchon, Republic of Korea, 200-701 E-mail:
More informationExtracting Spatio-temporal Local Features Considering Consecutiveness of Motions
Extracting Spatio-temporal Local Features Considering Consecutiveness of Motions Akitsugu Noguchi and Keiji Yanai Department of Computer Science, The University of Electro-Communications, 1-5-1 Chofugaoka,
More informationDevelopment in Object Detection. Junyuan Lin May 4th
Development in Object Detection Junyuan Lin May 4th Line of Research [1] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection, CVPR 2005. HOG Feature template [2] P. Felzenszwalb,
More informationCAP 6412 Advanced Computer Vision
CAP 6412 Advanced Computer Vision http://www.cs.ucf.edu/~bgong/cap6412.html Boqing Gong April 21st, 2016 Today Administrivia Free parameters in an approach, model, or algorithm? Egocentric videos by Aisha
More informationPartial Least Squares Regression on Grassmannian Manifold for Emotion Recognition
Emotion Recognition In The Wild Challenge and Workshop (EmotiW 2013) Partial Least Squares Regression on Grassmannian Manifold for Emotion Recognition Mengyi Liu, Ruiping Wang, Zhiwu Huang, Shiguang Shan,
More informationVIDEO AESTHETIC QUALITY ASSESSMENT USING KERNEL SUPPORT VECTOR MACHINE WITH ISOTROPIC GAUSSIAN SAMPLE UNCERTAINTY (KSVM-IGSU)
Proc. IEEE Int. Conf. on Image Processing (ICIP 016), Phoenix, AZ, USA, Sept. 016. DOI: 10.1109/ICIP.016.753791 016 IEEE. The published paper can be found at http://ieeexplore.ieee.org/document/753791/?arnumber=753791
More informationVideo Syntax Analysis
1 Video Syntax Analysis Wei-Ta Chu 2008/10/9 Outline 2 Scene boundary detection Key frame selection 3 Announcement of HW #1 Shot Change Detection Goal: automatic shot change detection Requirements 1. Write
More informationBus Detection and recognition for visually impaired people
Bus Detection and recognition for visually impaired people Hangrong Pan, Chucai Yi, and Yingli Tian The City College of New York The Graduate Center The City University of New York MAP4VIP Outline Motivation
More informationRegion-based Segmentation
Region-based Segmentation Image Segmentation Group similar components (such as, pixels in an image, image frames in a video) to obtain a compact representation. Applications: Finding tumors, veins, etc.
More informationC. Premsai 1, Prof. A. Kavya 2 School of Computer Science, School of Computer Science Engineering, Engineering VIT Chennai, VIT Chennai
Traffic Sign Detection Via Graph-Based Ranking and Segmentation Algorithm C. Premsai 1, Prof. A. Kavya 2 School of Computer Science, School of Computer Science Engineering, Engineering VIT Chennai, VIT
More informationDeep Learning For Video Classification. Presented by Natalie Carlebach & Gil Sharon
Deep Learning For Video Classification Presented by Natalie Carlebach & Gil Sharon Overview Of Presentation Motivation Challenges of video classification Common datasets 4 different methods presented in
More informationComputer Science Faculty, Bandar Lampung University, Bandar Lampung, Indonesia
Application Object Detection Using Histogram of Oriented Gradient For Artificial Intelegence System Module of Nao Robot (Control System Laboratory (LSKK) Bandung Institute of Technology) A K Saputra 1.,
More informationFace Recognition Using Vector Quantization Histogram and Support Vector Machine Classifier Rong-sheng LI, Fei-fei LEE *, Yan YAN and Qiu CHEN
2016 International Conference on Artificial Intelligence: Techniques and Applications (AITA 2016) ISBN: 978-1-60595-389-2 Face Recognition Using Vector Quantization Histogram and Support Vector Machine
More informationResearch on Evaluation Method of Video Stabilization
International Conference on Advanced Material Science and Environmental Engineering (AMSEE 216) Research on Evaluation Method of Video Stabilization Bin Chen, Jianjun Zhao and i Wang Weapon Science and
More informationCHAPTER 4 SEMANTIC REGION-BASED IMAGE RETRIEVAL (SRBIR)
63 CHAPTER 4 SEMANTIC REGION-BASED IMAGE RETRIEVAL (SRBIR) 4.1 INTRODUCTION The Semantic Region Based Image Retrieval (SRBIR) system automatically segments the dominant foreground region and retrieves
More informationA Cascade of Feed-Forward Classifiers for Fast Pedestrian Detection
A Cascade of eed-orward Classifiers for ast Pedestrian Detection Yu-ing Chen,2 and Chu-Song Chen,3 Institute of Information Science, Academia Sinica, aipei, aiwan 2 Dept. of Computer Science and Information
More informationFully Automatic Methodology for Human Action Recognition Incorporating Dynamic Information
Fully Automatic Methodology for Human Action Recognition Incorporating Dynamic Information Ana González, Marcos Ortega Hortas, and Manuel G. Penedo University of A Coruña, VARPA group, A Coruña 15071,
More informationHandwritten Script Recognition at Block Level
Chapter 4 Handwritten Script Recognition at Block Level -------------------------------------------------------------------------------------------------------------------------- Optical character recognition
More informationCursive Handwriting Recognition System Using Feature Extraction and Artificial Neural Network
Cursive Handwriting Recognition System Using Feature Extraction and Artificial Neural Network Utkarsh Dwivedi 1, Pranjal Rajput 2, Manish Kumar Sharma 3 1UG Scholar, Dept. of CSE, GCET, Greater Noida,
More informationCHAPTER 5 MOTION DETECTION AND ANALYSIS
CHAPTER 5 MOTION DETECTION AND ANALYSIS 5.1. Introduction: Motion processing is gaining an intense attention from the researchers with the progress in motion studies and processing competence. A series
More informationCS 221: Object Recognition and Tracking
CS 221: Object Recognition and Tracking Sandeep Sripada(ssandeep), Venu Gopal Kasturi(venuk) & Gautam Kumar Parai(gkparai) 1 Introduction In this project, we implemented an object recognition and tracking
More informationTEXT SEGMENTATION ON PHOTOREALISTIC IMAGES
TEXT SEGMENTATION ON PHOTOREALISTIC IMAGES Valery Grishkin a, Alexander Ebral b, Nikolai Stepenko c, Jean Sene d Saint Petersburg State University, 7 9 Universitetskaya nab., Saint Petersburg, 199034,
More informationCS 229 Final Project Report Learning to Decode Cognitive States of Rat using Functional Magnetic Resonance Imaging Time Series
CS 229 Final Project Report Learning to Decode Cognitive States of Rat using Functional Magnetic Resonance Imaging Time Series Jingyuan Chen //Department of Electrical Engineering, cjy2010@stanford.edu//
More informationHuman Action Recognition Using Silhouette Histogram
Human Action Recognition Using Silhouette Histogram Chaur-Heh Hsieh, *Ping S. Huang, and Ming-Da Tang Department of Computer and Communication Engineering Ming Chuan University Taoyuan 333, Taiwan, ROC
More informationOptical Flow-Based Person Tracking by Multiple Cameras
Proc. IEEE Int. Conf. on Multisensor Fusion and Integration in Intelligent Systems, Baden-Baden, Germany, Aug. 2001. Optical Flow-Based Person Tracking by Multiple Cameras Hideki Tsutsui, Jun Miura, and
More informationTri-modal Human Body Segmentation
Tri-modal Human Body Segmentation Master of Science Thesis Cristina Palmero Cantariño Advisor: Sergio Escalera Guerrero February 6, 2014 Outline 1 Introduction 2 Tri-modal dataset 3 Proposed baseline 4
More information2. LITERATURE REVIEW
2. LITERATURE REVIEW CBIR has come long way before 1990 and very little papers have been published at that time, however the number of papers published since 1997 is increasing. There are many CBIR algorithms
More informationMobile Human Detection Systems based on Sliding Windows Approach-A Review
Mobile Human Detection Systems based on Sliding Windows Approach-A Review Seminar: Mobile Human detection systems Njieutcheu Tassi cedrique Rovile Department of Computer Engineering University of Heidelberg
More informationInternational Journal of Modern Engineering and Research Technology
Volume 4, Issue 3, July 2017 ISSN: 2348-8565 (Online) International Journal of Modern Engineering and Research Technology Website: http://www.ijmert.org Email: editor.ijmert@gmail.com A Novel Approach
More informationLeveraging Textural Features for Recognizing Actions in Low Quality Videos
Leveraging Textural Features for Recognizing Actions in Low Quality Videos Saimunur Rahman, John See, Chiung Ching Ho Centre of Visual Computing, Faculty of Computing and Informatics Multimedia University,
More informationScanner Parameter Estimation Using Bilevel Scans of Star Charts
ICDAR, Seattle WA September Scanner Parameter Estimation Using Bilevel Scans of Star Charts Elisa H. Barney Smith Electrical and Computer Engineering Department Boise State University, Boise, Idaho 8375
More informationMATHEMATICAL IMAGE PROCESSING FOR AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM
J. KSIAM Vol.14, No.1, 57 66, 2010 MATHEMATICAL IMAGE PROCESSING FOR AUTOMATIC NUMBER PLATE RECOGNITION SYSTEM SUNHEE KIM, SEUNGMI OH, AND MYUNGJOO KANG DEPARTMENT OF MATHEMATICAL SCIENCES, SEOUL NATIONAL
More informationRobust PDF Table Locator
Robust PDF Table Locator December 17, 2016 1 Introduction Data scientists rely on an abundance of tabular data stored in easy-to-machine-read formats like.csv files. Unfortunately, most government records
More informationGraph Matching Iris Image Blocks with Local Binary Pattern
Graph Matching Iris Image Blocs with Local Binary Pattern Zhenan Sun, Tieniu Tan, and Xianchao Qiu Center for Biometrics and Security Research, National Laboratory of Pattern Recognition, Institute of
More informationCHAPTER 3. Preprocessing and Feature Extraction. Techniques
CHAPTER 3 Preprocessing and Feature Extraction Techniques CHAPTER 3 Preprocessing and Feature Extraction Techniques 3.1 Need for Preprocessing and Feature Extraction schemes for Pattern Recognition and
More informationDet De e t cting abnormal event n s Jaechul Kim
Detecting abnormal events Jaechul Kim Purpose Introduce general methodologies used in abnormality detection Deal with technical details of selected papers Abnormal events Easy to verify, but hard to describe
More informationResearch on Recognition and Classification of Moving Objects in Mixed Traffic Based on Video Detection
Hu, Qu, Li and Wang 1 Research on Recognition and Classification of Moving Objects in Mixed Traffic Based on Video Detection Hongyu Hu (corresponding author) College of Transportation, Jilin University,
More informationImproving Latent Fingerprint Matching Performance by Orientation Field Estimation using Localized Dictionaries
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 11, November 2014,
More informationA Semi-Automatic 2D-to-3D Video Conversion with Adaptive Key-Frame Selection
A Semi-Automatic 2D-to-3D Video Conversion with Adaptive Key-Frame Selection Kuanyu Ju and Hongkai Xiong Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China ABSTRACT To
More informationQuo Vadis, Action Recognition? A New Model and the Kinetics Dataset. By Joa õ Carreira and Andrew Zisserman Presenter: Zhisheng Huang 03/02/2018
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset By Joa õ Carreira and Andrew Zisserman Presenter: Zhisheng Huang 03/02/2018 Outline: Introduction Action classification architectures
More informationDetecting Printed and Handwritten Partial Copies of Line Drawings Embedded in Complex Backgrounds
9 1th International Conference on Document Analysis and Recognition Detecting Printed and Handwritten Partial Copies of Line Drawings Embedded in Complex Backgrounds Weihan Sun, Koichi Kise Graduate School
More informationMotion Tracking and Event Understanding in Video Sequences
Motion Tracking and Event Understanding in Video Sequences Isaac Cohen Elaine Kang, Jinman Kang Institute for Robotics and Intelligent Systems University of Southern California Los Angeles, CA Objectives!
More informationClassification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging
1 CS 9 Final Project Classification of Subject Motion for Improved Reconstruction of Dynamic Magnetic Resonance Imaging Feiyu Chen Department of Electrical Engineering ABSTRACT Subject motion is a significant
More informationDirectionally-grouped CHLAC Motion Feature Extraction and Its Application to Sport Motion Analysis
Directionally-grouped CHLAC Motion Feature Extraction and Its Application to Sport Motion Analysis FUMITO YOSHIKAWA, TAKUMI KOBAYASHI, KENJI WATANABE, NOBUYUKI OTSU National Institute of Advanced Industrial
More informationSuspicious Activity Detection of Moving Object in Video Surveillance System
International Journal of Latest Engineering and Management Research (IJLEMR) ISSN: 2455-4847 ǁ Volume 1 - Issue 5 ǁ June 2016 ǁ PP.29-33 Suspicious Activity Detection of Moving Object in Video Surveillance
More information28 IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 12, NO. 1, JANUARY 2010
28 IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 12, NO. 1, JANUARY 2010 Camera Motion-Based Analysis of User Generated Video Golnaz Abdollahian, Student Member, IEEE, Cuneyt M. Taskiran, Member, IEEE, Zygmunt
More informationUnsupervised Human Members Tracking Based on an Silhouette Detection and Analysis Scheme
Unsupervised Human Members Tracking Based on an Silhouette Detection and Analysis Scheme Costas Panagiotakis and Anastasios Doulamis Abstract In this paper, an unsupervised, automatic video human members(human
More informationPart-Based Skew Estimation for Mathematical Expressions
Soma Shiraishi, Yaokai Feng, and Seiichi Uchida shiraishi@human.ait.kyushu-u.ac.jp {fengyk,uchida}@ait.kyushu-u.ac.jp Abstract We propose a novel method for the skew estimation on text images containing
More informationAutomatic Seamless Face Replacement in Videos
Automatic Seamless Face Replacement in Videos Yiren Lu, Dongni Wang University of Pennsylvania December 2016 1 Introduction In this project, the goal is automatic face detection and replacement in videos.
More informationActive Query Sensing for Mobile Location Search
(by Mac Funamizu) Active Query Sensing for Mobile Location Search Felix. X. Yu, Rongrong Ji, Shih-Fu Chang Digital Video and Multimedia Lab Columbia University November 29, 2011 Outline Motivation Problem
More informationLarge-scale Video Classification with Convolutional Neural Networks
Large-scale Video Classification with Convolutional Neural Networks Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-Fei Note: Slide content mostly from : Bay Area
More informationAN EFFICIENT BINARIZATION TECHNIQUE FOR FINGERPRINT IMAGES S. B. SRIDEVI M.Tech., Department of ECE
AN EFFICIENT BINARIZATION TECHNIQUE FOR FINGERPRINT IMAGES S. B. SRIDEVI M.Tech., Department of ECE sbsridevi89@gmail.com 287 ABSTRACT Fingerprint identification is the most prominent method of biometric
More informationLECTURE 6 TEXT PROCESSING
SCIENTIFIC DATA COMPUTING 1 MTAT.08.042 LECTURE 6 TEXT PROCESSING Prepared by: Amnir Hadachi Institute of Computer Science, University of Tartu amnir.hadachi@ut.ee OUTLINE Aims Character Typology OCR systems
More informationChapter 3 Image Registration. Chapter 3 Image Registration
Chapter 3 Image Registration Distributed Algorithms for Introduction (1) Definition: Image Registration Input: 2 images of the same scene but taken from different perspectives Goal: Identify transformation
More informationBasic relations between pixels (Chapter 2)
Basic relations between pixels (Chapter 2) Lecture 3 Basic Relationships Between Pixels Definitions: f(x,y): digital image Pixels: q, p (p,q f) A subset of pixels of f(x,y): S A typology of relations:
More information[2008] IEEE. Reprinted, with permission, from [Yan Chen, Qiang Wu, Xiangjian He, Wenjing Jia,Tom Hintz, A Modified Mahalanobis Distance for Human
[8] IEEE. Reprinted, with permission, from [Yan Chen, Qiang Wu, Xiangian He, Wening Jia,Tom Hintz, A Modified Mahalanobis Distance for Human Detection in Out-door Environments, U-Media 8: 8 The First IEEE
More informationNeural Network based textural labeling of images in multimedia applications
Neural Network based textural labeling of images in multimedia applications S.A. Karkanis +, G.D. Magoulas +, and D.A. Karras ++ + University of Athens, Dept. of Informatics, Typa Build., Panepistimiopolis,
More informationDT-Binarize: A Hybrid Binarization Method using Decision Tree for Protein Crystallization Images
DT-Binarize: A Hybrid Binarization Method using Decision Tree for Protein Crystallization Images İmren Dinç 1, Semih Dinç 1, Madhav Sigdel 1, Madhu S. Sigdel 1, Marc L. Pusey 2, Ramazan S. Aygün 1 1 DataMedia
More informationSelf Lane Assignment Using Smart Mobile Camera For Intelligent GPS Navigation and Traffic Interpretation
For Intelligent GPS Navigation and Traffic Interpretation Tianshi Gao Stanford University tianshig@stanford.edu 1. Introduction Imagine that you are driving on the highway at 70 mph and trying to figure
More informationIntroduction to Medical Imaging (5XSA0) Module 5
Introduction to Medical Imaging (5XSA0) Module 5 Segmentation Jungong Han, Dirk Farin, Sveta Zinger ( s.zinger@tue.nl ) 1 Outline Introduction Color Segmentation region-growing region-merging watershed
More informationDeveloping Open Source code for Pyramidal Histogram Feature Sets
Developing Open Source code for Pyramidal Histogram Feature Sets BTech Project Report by Subodh Misra subodhm@iitk.ac.in Y648 Guide: Prof. Amitabha Mukerjee Dept of Computer Science and Engineering IIT
More informationDeep Tracking: Biologically Inspired Tracking with Deep Convolutional Networks
Deep Tracking: Biologically Inspired Tracking with Deep Convolutional Networks Si Chen The George Washington University sichen@gwmail.gwu.edu Meera Hahn Emory University mhahn7@emory.edu Mentor: Afshin
More informationDETECTION OF SMOOTH TEXTURE IN FACIAL IMAGES FOR THE EVALUATION OF UNNATURAL CONTRAST ENHANCEMENT
DETECTION OF SMOOTH TEXTURE IN FACIAL IMAGES FOR THE EVALUATION OF UNNATURAL CONTRAST ENHANCEMENT 1 NUR HALILAH BINTI ISMAIL, 2 SOONG-DER CHEN 1, 2 Department of Graphics and Multimedia, College of Information
More informationAUTOMATIC VIDEO INDEXING
AUTOMATIC VIDEO INDEXING Itxaso Bustos Maite Frutos TABLE OF CONTENTS Introduction Methods Key-frame extraction Automatic visual indexing Shot boundary detection Video OCR Index in motion Image processing
More informationClustering Large Credit Client Data Sets for Classification with SVM
Clustering Large Credit Client Data Sets for Classification with SVM Ralf Stecking University of Oldenburg Department of Economics Klaus B. Schebesch University Vasile Goldiş Arad Faculty of Economics
More informationA Survey on Face-Sketch Matching Techniques
A Survey on Face-Sketch Matching Techniques Reshma C Mohan 1, M. Jayamohan 2, Arya Raj S 3 1 Department of Computer Science, SBCEW 2 Department of Computer Science, College of Applied Science 3 Department
More informationFacial Expression Classification with Random Filters Feature Extraction
Facial Expression Classification with Random Filters Feature Extraction Mengye Ren Facial Monkey mren@cs.toronto.edu Zhi Hao Luo It s Me lzh@cs.toronto.edu I. ABSTRACT In our work, we attempted to tackle
More informationInternational Journal of Innovative Research in Computer and Communication Engineering
Moving Object Detection By Background Subtraction V.AISWARYA LAKSHMI, E.ANITHA, S.SELVAKUMARI. Final year M.E, Department of Computer Science and Engineering Abstract : Intelligent video surveillance systems
More informationGenetic Algorithm for Seismic Velocity Picking
Proceedings of International Joint Conference on Neural Networks, Dallas, Texas, USA, August 4-9, 2013 Genetic Algorithm for Seismic Velocity Picking Kou-Yuan Huang, Kai-Ju Chen, and Jia-Rong Yang Abstract
More informationObject Tracking using HOG and SVM
Object Tracking using HOG and SVM Siji Joseph #1, Arun Pradeep #2 Electronics and Communication Engineering Axis College of Engineering and Technology, Ambanoly, Thrissur, India Abstract Object detection
More informationImage Segmentation. 1Jyoti Hazrati, 2Kavita Rawat, 3Khush Batra. Dronacharya College Of Engineering, Farrukhnagar, Haryana, India
Image Segmentation 1Jyoti Hazrati, 2Kavita Rawat, 3Khush Batra Dronacharya College Of Engineering, Farrukhnagar, Haryana, India Dronacharya College Of Engineering, Farrukhnagar, Haryana, India Global Institute
More informationRandom spatial sampling and majority voting based image thresholding
1 Random spatial sampling and majority voting based image thresholding Yi Hong Y. Hong is with the City University of Hong Kong. yihong@cityu.edu.hk November 1, 7 2 Abstract This paper presents a novel
More informationConvolution Neural Networks for Chinese Handwriting Recognition
Convolution Neural Networks for Chinese Handwriting Recognition Xu Chen Stanford University 450 Serra Mall, Stanford, CA 94305 xchen91@stanford.edu Abstract Convolutional neural networks have been proven
More informationVisual Attention Control by Sensor Space Segmentation for a Small Quadruped Robot based on Information Criterion
Visual Attention Control by Sensor Space Segmentation for a Small Quadruped Robot based on Information Criterion Noriaki Mitsunaga and Minoru Asada Dept. of Adaptive Machine Systems, Osaka University,
More informationTraining-Free, Generic Object Detection Using Locally Adaptive Regression Kernels
Training-Free, Generic Object Detection Using Locally Adaptive Regression Kernels IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIENCE, VOL.32, NO.9, SEPTEMBER 2010 Hae Jong Seo, Student Member,
More informationA novel template matching method for human detection
University of Wollongong Research Online Faculty of Informatics - Papers (Archive) Faculty of Engineering and Information Sciences 2009 A novel template matching method for human detection Duc Thanh Nguyen
More informationPreliminary Local Feature Selection by Support Vector Machine for Bag of Features
Preliminary Local Feature Selection by Support Vector Machine for Bag of Features Tetsu Matsukawa Koji Suzuki Takio Kurita :University of Tsukuba :National Institute of Advanced Industrial Science and
More informationBeyond Bags of Features
: for Recognizing Natural Scene Categories Matching and Modeling Seminar Instructed by Prof. Haim J. Wolfson School of Computer Science Tel Aviv University December 9 th, 2015
More informationBased on Multi-Modal Violent Movies Detection in Video Sharing Sites
Based on Multi-Modal Violent Movies Detection in Video Sharing Sites Xingyu Zou 1, Ou Wu 2, Qishen Wang 2, Weiming Hu 2, Jinfeng Yang 1 1 College of aviation automation, Civil Aviation University of China,
More informationTime Stamp Detection and Recognition in Video Frames
Time Stamp Detection and Recognition in Video Frames Nongluk Covavisaruch and Chetsada Saengpanit Department of Computer Engineering, Chulalongkorn University, Bangkok 10330, Thailand E-mail: nongluk.c@chula.ac.th
More informationAutomatic Video Caption Detection and Extraction in the DCT Compressed Domain
Automatic Video Caption Detection and Extraction in the DCT Compressed Domain Chin-Fu Tsao 1, Yu-Hao Chen 1, Jin-Hau Kuo 1, Chia-wei Lin 1, and Ja-Ling Wu 1,2 1 Communication and Multimedia Laboratory,
More informationCopyright Detection System for Videos Using TIRI-DCT Algorithm
Research Journal of Applied Sciences, Engineering and Technology 4(24): 5391-5396, 2012 ISSN: 2040-7467 Maxwell Scientific Organization, 2012 Submitted: March 18, 2012 Accepted: June 15, 2012 Published:
More informationLearning to Recognize Faces in Realistic Conditions
000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050
More informationVideo De-interlacing with Scene Change Detection Based on 3D Wavelet Transform
Video De-interlacing with Scene Change Detection Based on 3D Wavelet Transform M. Nancy Regina 1, S. Caroline 2 PG Scholar, ECE, St. Xavier s Catholic College of Engineering, Nagercoil, India 1 Assistant
More informationA reversible data hiding based on adaptive prediction technique and histogram shifting
A reversible data hiding based on adaptive prediction technique and histogram shifting Rui Liu, Rongrong Ni, Yao Zhao Institute of Information Science Beijing Jiaotong University E-mail: rrni@bjtu.edu.cn
More informationMethodology for spot quality evaluation
Methodology for spot quality evaluation Semi-automatic pipeline in MAIA The general workflow of the semi-automatic pipeline analysis in MAIA is shown in Figure 1A, Manuscript. In Block 1 raw data, i.e..tif
More informationClassification with Class Overlapping: A Systematic Study
Classification with Class Overlapping: A Systematic Study Haitao Xiong 1 Junjie Wu 1 Lu Liu 1 1 School of Economics and Management, Beihang University, Beijing 100191, China Abstract Class overlapping has
More information