AUTOMATED VIDEO INDEXING AND VIDEO SEARCH IN LARGE LECTURE VIDEO ARCHIVES USING HADOOP FRAMEWORK
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1 AUTOMATED VIDEO INDEXING AND VIDEO SEARCH IN LARGE LECTURE VIDEO ARCHIVES USING HADOOP FRAMEWORK P. Satya Shekar Varma 1,Prof.K.VenkateshwarRao 2,A.SaiPhanindra 3,G. Ritin Surya Sainadh 4 1,3,4 Department of Computer Science & Engineering MGIT, Hyderabad, Telangana, India. 2 Department of Computer Science & Engineering JNTU, Hyderabad, Telangana, India. ABSTRACT: Still it is a difficult problem to index video archives for future use. Generally, lecture videos contain slides and explanation of them is done. The text displayed in those slides are the main part of video content. Therefore, video text can be used for indexing the videos in a large video data archives. In this paper, we propose a method for automated lecture video indexing and video content browsing and video search. Keywords: Lecture Video Archives, Video Segmentation, Stroke Width Transform, Connected Components [1] INTRODUCTION The development of recording technology, compression of video and network speed, audio-video recordings are used very frequently in e-lecturing systems. Which led to tremendous increase in multimedia data on the web. It is very tedious task to find desired or required videos within a video archive without any search function. The video user would require information being a small part of the entire video without viewing the whole video archive. So an internal tag based technique is needed. And the process of tagging and assigning for huge number of videos requires resources and high computational power. So a unique system cannot lead to a feasible solution. Therefore, a distributed approach is taken, wherein number of system are used as nodes in Hadoop cluster-setup, thereby required computational power is provided for the process to run. Why not General CBVR? In general, Content Based Video Retrieval (CBVR) works by parsing a video, its Abstraction, Content analysis etc. Which are not very effective in case of huge lecture video archives, and it is assumed that lecture videos are consisting of slides text. Hence a specialized video segmentation techniq0ue to get individual slides is applied. Big Data: It is a broad term for data sets which are large that traditional data processing applications fail to handle. Data sets grow in size in part because they are being gathered by many application resources. There are mainly three key differences in big data. They are: Volume: The exponential growth in data storage is the volume. Now-a- days Terabytes and Petabytes of the storage system for enterprises is very common. 33
2 Velocity: The data generation is so fast in many applications. The data movement or the flow of data is massive and continuous. Variety: We can find data in the format of videos, music and large images on our social media channels. In the real world we have data in many different formats and that is the challenge we need to overcome with the Big Data. There is much advancement in big data analysis which offers cost-effective opportunities to improve critical development and also decision-making in areas like productivity, security, health care, crime and natural disaster. Hadoop: It is designed to efficiently distribute large amounts of data across a group of machines and well also to efficiently process them to work in parallel. It saves a lot of time and money. It uses a large-scale distributed batch processing infrastructure and an open source programming framework. It also facilitates to continue operating without interruption in case of a node failure also. Hadoop common: Hadoop Common refers to the collection of common utilities and libraries that are supported to Hadoop modules. It assumes that hardware failures are common and that these should be automatically handled in software by the Hadoop Framework. HDFS: Hadoop Distributed File System (HDFS) is a file system uses all the nodes in a Hadoop cluster together for data storage. It also stores as replication of data which may be recovered at the time of node failure. YARN: Yet Another Resource Negotiator (YARN) assigns and also manages CPU, memory and storage to applications running on a Hadoop cluster. The initial stage of Hadoop could only run MapReduce applications but YARN enables other application frameworks to run on Hadoop. MapReduce: MapReduce is a programming model and an associated implementation for processing and generating large data sets with a parallel, distributed algorithm on a cluster. A MapReduce program is consists of a Map () phase that performs filtering and sorting and a Reduce () phase that performs a summarization of operations. It provides redundancy and fault tolerance. Big Video Processing: Basically, the lecture videos archives contain several hours of lectures recorded. So to process these huge datasets Hadoop framework is adopted. Hadoop breaks the bit stream chunks in parallel manner and produces a sequence file. It stores huge files in a distributed file system called Hadoop Distributed File System (HDFS) as small chunks of certain block size across a cluster of commodity hardware. In order to process such huge video datasets, we are using the Hadoop Map Reduce framework. [2] METHODOLOGY If there is a huge dataset there is a problem of storing and processing the data and Hadoop is the best solution for these ever. The metadata extracted is the crucial way to find a particular video matching in a large scale database of lecture videos. Initially, from the visual screen we detect the slide transitions and extract each unique slide frame with its temporal scope considered as the video segment. A. Segmentation of video into slides and Recognition of keywords 34
3 Here in this phase we perform segmentation of video into representative key frames. Some frames are selected as key frames which can provide a visual guideline for retrieving of the required lecture video. After this stage the key frames are subjected to OCR. As the basic element for differencing analysis, instead of pixel we used Connected Components (CCs). We can also have called it as component-level-differencing metric. The salt and pepper noise can be rejected from the differencing process and robustness is also increased, in this way we can also control the valid size of the CC. Finally, the unique slides with total content are recognized and captured as video segments. In the first step of segmentation, the entire slide video is analyzed. Change between adjacent frames is observed, by taking both accuracy and efficiency into account we had established an interval of three seconds. For the adjacent observed frames, we create canny edge maps and make pixel differential image from those canny edge maps. Eventually, the Connected Components (CCs) analysis is performed on this differential image and Connected Components (CCs) number is used as a threshold value for segmentation. In the second step of segmentation, the real slide transitions will be captured. First, the title region and content region of slide frame is defined. The content distribution of commonly used slide styles is established by analysis of large-scale lecture videos. This is the process of segmentation of video into slides. Figure: 1. Flow of Video Segmentation In retrieval task the important thing we took is text given in the slides of lecture video, because they are more closely related to the content explained in the video. A new verification scheme is used for detecting the text. To quickly localize candidate text regions an edge-based multi-scale text detector is used with a low rejection rate. To reject false positives that exposes low edge density and to split text and non-text regions into separate blocks an image entropybased adaptive refinement algorithm is used [3]. Finally, to remove the non-text area blocks Stroke Width Transform (SWT) is applied. The SWT verifier fails to identify special non-text patterns such as garden fence, sphere etc. To ameliorate the detection accuracy, we had adopted an additional SVM classifier to remove these non-text areas which SWT cannot remove. For recognition of text regions, we had developed a binarization approach, so that for identification of text pixels we use canny edge maps which are generated previously. This approach mainly contains three steps: seed pixel selection, text gradient direction analysis and seed-region growing. After the end of this process, the video text images are converted into a compatible format for standard OCR. To find out the incorrect words from the results we are also having spell-checking process. B. Structural Analysis of slides Initially, identification of title line procedure is done. If a text line is in the upper third part of the frame and has more than three characters, then it is considered as title line. Based 35
4 on the height and average stroke width of text line all non-title lines are divided into three types they are: key point, content text and foot line. [7] key-point if st > smean ^ ht > hmean footline if st < smean ^ ht < hmean ^ y = ymax content text otherwise; Where smean and hmean denote the average stroke width and the average text line height of a slide frame, and ymax denotes the maximum vertical position of a text line object. Then spell check is applied. C. Browsing Video Content with segmented key frames Within the video player we can find out the segments of video, this is introduced to have a fast navigation. A slide gallery is also provided below the video player, so that if user need to study the slide content clearly he/she can go through it. On clicking the time line units the video navigation is done better. D. Keyword Extraction and Video Search Hence the metadata can be generated by output of OCR. Many times, the recognized words may be irrelevant to the content or errors. So, from the raw results of recognized words we extract some keywords. Finally, those extracted key words are summed up and are useful to retrieve the video from a large lecture video archives. Majority of extracted key words are nouns and spelling errors can also be removed by applying OCR. Because a dictionary based filtering process is done. [3] RESULTS AND DISCUSSIONS Input Data: A presentation video of a power point presentation using generate video feature of Ms power point. Output: Metadata is generated for the videos and stored in a database. Video Segmentation: Following are the slides detected by video segmentation. Figure: 2.: Slides detected by video segmentation. Separating non-text areas: Input: The input for this step is all the key frames extracted from the previous step. These key frames are all the distinct slides present in the video. Output: We apply Stroke Width Transform to all these key frames. The result of this is that all the non-text areas like images, logos, etc. are eliminated from the frames and the text areas are. 36
5 Figure: 3. An extracted Key Frame. Figure: 4. Text areas of the Key Frame. Recognizing words and sentences: Input: After separating all the non-text areas from the text areas in the key frames, the resultant frames are inputted in this step. Output: Using Connected Components and the distance between individual letters, all the words are distinguished from each other and a box is drawn around each word. Following slide contains recognised words and sentences. Figure: 5. Slide contains recognized words and sentences. Generating attributes of words detected: Input: All the words recognized in the previous step are given as input in this step along with the attributes of the slides they are present to process them Output: OCR is applied on each of the word recognized to eliminate errors in words. Then attributes of each word such as position, slide number, etc. are found. Valid words filtered from Dictionary after OCR Detection. 37
6 Figure: 6. Valid words filtered from Dictionary after OCR Detection. Assigning weights to words: Input: The attributes of words detected. Output: The weights assigned to each word based on their attributes. Average heights of text (font height) for each slide Figure: 7. Average heights of text (font height) for each slide. [4] CONCLUSION Using this method videos can be tagged automatically based on content they have in their slides. This paper provides an easy way to navigate the exacted video where our query is present and also go to the exact location in terms on time elapsed where the search query is first encountered. ACKNOWLEDGEMENT The authors would like to thank D. Vijay Kumar for his invaluable guidance which led to improvise the quality of this paper. REFERENCES [1] M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, Content-Based Image Retrieval at the End of the Early Years, IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12,pp , Dec [2] Ikeda, Osamu. "Segmentation of faces in video footage using HSV color for face detection and image retrieval." Image Processing, ICIP Proceedings. 2003International Conference on. Vol. 3. IEEE, [3] Doulamis, A., and N. Doulamis. "Optimal content-based video decomposition for interactive video navigation." Circuits and Systems for Video Technology, IEEETransactions on 14.6 (2004): [4] Padmakala, S., G. S. AnandhaMala, and M. Shalini. "An effective content based video retrieval utilizing texture, color and optimal key frame features."image Information Processing (ICIIP), 2011 International Conference on. IEEE,
7 [5] H. Yang, B. Quehl, and H. Sack. (2012), A framework for improved video text detection and recognition, Multimedia Tools Appl., pp. 1 29, [Online]. Available: [6] Chaisorn, Lekha, Corey Manders, and SusantoRahardja. "Video retrieval-evolution of video segmentation, indexing and search." Computer Science and Information Technology, ICCSIT nd IEEE International Conference on. IEEE, [7] Haojin Yang and Christoph Meinel, Content Based Lecture Video Retrieval Using Speech and Video Text Information, IEEE Transactions on Learning Technologies, Vol. 7, No.2, April-June, [8] H. J. Jeong, T.-E. Kim, and M. H. Kim.(2012), An accurate lecture video segmentation method by using sift and adaptive threshold, in Proc. 10th Int. Conf. Advances Mobile Comput., pp [Online].Available: [9] H. Sack and J. Waitelonis, Integrating social tagging and document annotation for contentbased search in multimedia data, in Proc. 1st Semantic Authoring Annotation Workshop, [10] G.Salton and C. Buckley, Term-weighting approaches in automatic text retrieval, Inf. Process. Manage., vol. 24, no. 5, pp , [11] B. Epshtein, E. Ofek, and Y. Wexler, Detecting text in natural scenes with stroke width transform, in Proc. Int. Conf. Computation. Vis. Pattern Recognition, San Francisco, CA, June [12] F. Chang, C.-J. Chen, and C.-J. Lu, A linear-time componentlabeling algorithm using contour tracing technique, Comput. Vis.Image Understanding, vol. 93, no. 2, pp ,Jan [13] B. T. N. Dala, Histograms of oriented gradients for human detection, in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2005,vol. 1, pp [14] Ground truthdata.(2013).[online]. Available: [15] C. Meinel, F. Moritz, and M. Siebert, Community tagging in tele-teaching environments, in Proc. 2nd Int. Conf. e-educ., e-bus.,e-manage. and E-Learn., [16] S. Repp, A. Gross, and C. Meinel, Browsing within lecture videos based on the chain index of speech transcription, IEEE Trans.Learn. Technol., vol. 1, no. 3, pp , Jul
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