Hands On: Multimedia Methods for Large Scale Video Analysis (Project Meeting) Dr. Gerald Friedland,
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1 Hands On: Multimedia Methods for Large Scale Video Analysis (Project Meeting) Dr. Gerald Friedland, 1
2 Today
3 Today Project Requirements
4 Today Project Requirements Data available
5 Today Project Requirements Data available Compute Architecture at ICSI
6 Today Project Requirements Data available Compute Architecture at ICSI Some Project Ideas
7 Project Requirements 3
8 Project Requirements Form a team of 2 to 3 people. 3
9 Project Requirements Form a team of 2 to 3 people. Each team work on one project idea 3
10 Project Requirements Form a team of 2 to 3 people. Each team work on one project idea Project must be on big multimedia data, e.g. at least ten-thousands of videos, hundred-thousands of sounds pieces/images. 3
11 Project Requirements Form a team of 2 to 3 people. Each team work on one project idea Project must be on big multimedia data, e.g. at least ten-thousands of videos, hundred-thousands of sounds pieces/images. Team delivers written project report at the end of semester, reports on progress during the semester 3
12 Each Project Report... 4
13 Each Project Report......needs to explain the idea of the project and show evidence that the project has been performed. 4
14 Each Project Report......needs to explain the idea of the project and show evidence that the project has been performed....needs to report on 4
15 Each Project Report......needs to explain the idea of the project and show evidence that the project has been performed....needs to report on accuracy 4
16 Each Project Report......needs to explain the idea of the project and show evidence that the project has been performed....needs to report on accuracy efficiency 4
17 Each Project Report......needs to explain the idea of the project and show evidence that the project has been performed....needs to report on accuracy efficiency scalability 4
18 Each Project Report......needs to explain the idea of the project and show evidence that the project has been performed....needs to report on accuracy efficiency scalability limits of the approach 4
19 Each Project Report......needs to explain the idea of the project and show evidence that the project has been performed....needs to report on accuracy efficiency scalability limits of the approach... and reports on problems occured. 4
20 Project Resources 5
21 Data: Project Resources 5
22 Data: Project Resources Use the data provided in the class (TrecVID, 1M Songs, MediaEval) 5
23 Data: Project Resources Use the data provided in the class (TrecVID, 1M Songs, MediaEval) Use your own (after discussion with instructor) 5
24 Data: Project Resources Use the data provided in the class (TrecVID, 1M Songs, MediaEval) Use your own (after discussion with instructor) Compute: 5
25 Data: Project Resources Use the data provided in the class (TrecVID, 1M Songs, MediaEval) Use your own (after discussion with instructor) Compute: Use your own laptop initially then 5
26 Data: Project Resources Use the data provided in the class (TrecVID, 1M Songs, MediaEval) Use your own (after discussion with instructor) Compute: Use your own laptop initially then Use ICSI s compute pool then 5
27 Data: Project Resources Use the data provided in the class (TrecVID, 1M Songs, MediaEval) Use your own (after discussion with instructor) Compute: Use your own laptop initially then Use ICSI s compute pool then if needed, use Amazon EC2 5
28 Data: Project Resources Use the data provided in the class (TrecVID, 1M Songs, MediaEval) Use your own (after discussion with instructor) Compute: Use your own laptop initially then Use ICSI s compute pool then if needed, use Amazon EC2 Use any other compute resource that you have access to. 5
29 Project Resources II 6
30 Project Resources II Hard disk: 6
31 Project Resources II Hard disk: Once in the ICSI phase use ICSI s ttmp structure 6
32 Project Resources II Hard disk: Once in the ICSI phase use ICSI s ttmp structure At Amazon: Need to buy space if needed. 6
33 Project Resources II Hard disk: Once in the ICSI phase use ICSI s ttmp structure At Amazon: Need to buy space if needed. Time: 6
34 Project Resources II Hard disk: Once in the ICSI phase use ICSI s ttmp structure At Amazon: Need to buy space if needed. Time: Start work on your project as early as possible! 6
35 Project Resources II Hard disk: Once in the ICSI phase use ICSI s ttmp structure At Amazon: Need to buy space if needed. Time: Start work on your project as early as possible! 6
36 Project Resources II Hard disk: Once in the ICSI phase use ICSI s ttmp structure At Amazon: Need to buy space if needed. Time: Start work on your project as early as possible! 6
37 Project Resources II Hard disk: Once in the ICSI phase use ICSI s ttmp structure At Amazon: Need to buy space if needed. Time: Start work on your project as early as possible! 6
38 Project Resources II Hard disk: Once in the ICSI phase use ICSI s ttmp structure At Amazon: Need to buy space if needed. Time: Start work on your project as early as possible! 6
39 Project Idea 7
40 Project Idea Come up with your own project idea as a team, inspired by the class content, co-students, the data, and/or other input. 7
41 Project Idea Come up with your own project idea as a team, inspired by the class content, co-students, the data, and/or other input. Discuss the project idea with the class and the instructor 7
42 Available BIGDATA 8
43 Available BIGDATA MediaEval
44 Available BIGDATA MediaEval 2012 TrecVid MED
45 Available BIGDATA MediaEval 2012 TrecVid MED M Songs 8
46 Placing Task Automatically guess the location of a Flickr video: i.e., assign geo-coordinates (latitude and longitude) Using one or more of: Visual/Audio content Metadata (title, tags, description, etc) Social information 9
47 Data Description (2010) Training Data 15k videos/metadata/visual keyframes (+features)/geo-tags 6M photos/metadata/visual features Test Data 5k video/metadata/visual keyframes (+features) no geotags Test/Training split: by UserID 10
48 Example 11
49 A Good video Title: CIMG0254 Keywords: southamerica, june, 2008, video, pearce, vacation, iguazufalls, iguassufalls, iguaçufalls, waterfall, argentina Description: (None) 14
50 How about this one? Title: MVI_6423_rau Keywords: usa08, puertorico Description: (None) 15
51 Another bad video Title: Stillness #1 Keywords: henrywcoestatepark, henrycoe, california, 2009, hiking, landscape, nature, chinaholetrail, video Description: When I hike, I like to stop at random spots and just stand still for a few minutes and listen and look at my surroundings. On this hike, I decided to record a few seconds during those moments. 16
52 Metadata 98.8% of videos were annotated with at least one title, tags, or description 17
53 TrecVID MED 2011 detailed 16
54 TrecVID MED 2011 detailed Videos all consumer produced, typically 1-5 minutes long 16
55 TrecVID MED 2011 detailed Videos all consumer produced, typically 1-5 minutes long Given 15 concepts, 5 for training, 10 for eval 16
56 TrecVID MED 2011 detailed Videos all consumer produced, typically 1-5 minutes long Given 15 concepts, 5 for training, 10 for eval About 100 sample videos per concept 16
57 TrecVID MED 2011 detailed Videos all consumer produced, typically 1-5 minutes long Given 15 concepts, 5 for training, 10 for eval About 100 sample videos per concept Testset 2011: 50k videos, open set 16
58 TrecVID Dataset Consumer-Produced, Unfiltered Videos... 17
59 What is Video Concept Detection? 18
60 What is Video Concept Detection? A concept (as of TrecVID MED 11): 18
61 What is Video Concept Detection? A concept (as of TrecVID MED 11): is a complex activity occurring at a specific place and time; 18
62 What is Video Concept Detection? A concept (as of TrecVID MED 11): is a complex activity occurring at a specific place and time; involves people interacting with other people and/or objects; 18
63 What is Video Concept Detection? A concept (as of TrecVID MED 11): is a complex activity occurring at a specific place and time; involves people interacting with other people and/or objects; consists of a number of human actions, processes, and activities that are loosely or tightly organized and that have significant temporal and semantic relationships to the overarching activity; 18
64 What is Video Concept Detection? A concept (as of TrecVID MED 11): is a complex activity occurring at a specific place and time; involves people interacting with other people and/or objects; consists of a number of human actions, processes, and activities that are loosely or tightly organized and that have significant temporal and semantic relationships to the overarching activity; is directly observable. 18
65 Event Kit? 19
66 Event Kit? An Event Kit consists of: 19
67 Event Kit? An Event Kit consists of: A textual (natural language) event definition 19
68 Event Kit? An Event Kit consists of: A textual (natural language) event definition A textual (NL) event explication, which is a glossart for the definition 19
69 Event Kit? An Event Kit consists of: A textual (natural language) event definition A textual (NL) event explication, which is a glossart for the definition Evidential description: A textual listing of attributes that are indicative of an event instance. 19
70 Event Kit? An Event Kit consists of: A textual (natural language) event definition A textual (NL) event explication, which is a glossart for the definition Evidential description: A textual listing of attributes that are indicative of an event instance. A set of illustrative video examples each containing an instance of the event. 19
71 Event Kit: Example Event name: Attempting a board trick Definition: One or more people attempt to do a trick on a skateboard, snowboard, surfboard, or other boardsport board. Explication: Boardsports are sports where a person stands, sits, or lays on a board and moves and controls the board. Tricks consist of intentional motions made with the board that are not simply slowing down/stopping the board or steering the board as it moves. Steering around obstacles or steering a board off of a jump and landing on the ground are not considered tricks in and of themselves. Common tricks involve actions like sliding the board along the top of an object (e.g. a swimming pool rim or railing), jumping from the ground or the surface of water into the air, and spinning or flipping in the air. Evidential description: scene: outside, often in a skatepark objects/people: skateboard, snowboard, surfboard, ramps, rails, safety gear, crowds activities: standing, sitting or laying on the board; jumping with the board; flipping the board and landing on it; spinning the board; sliding the board across various objects. audio: sounds of board hitting surface during trick; crowd cheering 20
72 2011 Concepts Event Category Train DevTest E001 Board Tricks E002 Feeding Animal E003 Landing a Fish E004 Wedding E005 Woodworking E006 Birthday Party E007 Changing Tire E008 Flash Mob E009 Vehicle Unstuck E010 Grooming animal E011 Make a Sandwich E012 Parade E013 Parkour E014 Repairing Appliance E015 Sewing Other Random other N/A
73 Sample Video 1: Board Tricks 22
74 Sample Video 2: Board Tricks 23
75 Test Video: Board Tricks 24
76 Test Video: Board Tricks NOT A POSITIVE! 24
77 25
78 The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. 25
79 The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. Its purposes are: 25
80 The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. Its purposes are: To encourage research on algorithms that scale to commercial sizes 25
81 The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. Its purposes are: To encourage research on algorithms that scale to commercial sizes To provide a reference dataset for evaluating research 25
82 The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. Its purposes are: To encourage research on algorithms that scale to commercial sizes To provide a reference dataset for evaluating research As a shortcut alternative to creating a large dataset with APIs (e.g. The Echo Nest's) 25
83 The Million Song Dataset is a freely-available collection of audio features and metadata for a million contemporary popular music tracks. Its purposes are: To encourage research on algorithms that scale to commercial sizes To provide a reference dataset for evaluating research As a shortcut alternative to creating a large dataset with APIs (e.g. The Echo Nest's) To help new researchers get started in the MIR field 25
84 26
85 Dataset is a sqllite database of about 100 attributes for each song. 26
86 Dataset is a sqllite database of about 100 attributes for each song. Subset of 10k song attributes available 26
87 Dataset is a sqllite database of about 100 attributes for each song. Subset of 10k song attributes available The song IS NOT part of the database. Need to access using a different database using metadata (e.g. Grooveshark). 26
88 27
89 Compute Architecture at ICSI
90 Compute Architecture at ICSI Each enrolled student gets account at ICSI
91 Compute Architecture at ICSI Each enrolled student gets account at ICSI ICSI = International Computer Science Institute: Location: 1947 Center Street, 6th floor
92 Compute Architecture at ICSI
93 Compute Architecture at ICSI Accounts belong to speech group and allow access to Unix compute cluster of that group
94 Compute Architecture at ICSI Accounts belong to speech group and allow access to Unix compute cluster of that group Compute Cluster is currently 160 CPUs
95 Compute Architecture at ICSI Accounts belong to speech group and allow access to Unix compute cluster of that group Compute Cluster is currently 160 CPUs Main compute: Squids 8x8 CPUs, 8x2 GPUs
96 Compute Architecture at ICSI
97 Compute Architecture at ICSI Most important page for technical Info: speechwiki/index.php/main_page
98 Compute Architecture at ICSI Most important page for technical Info: speechwiki/index.php/main_page Includes Ganglia Monitoring
99 Compute Architecture at ICSI Most important page for technical Info: speechwiki/index.php/main_page Includes Ganglia Monitoring
100 Compute Architecture at ICSI Most important page for technical Info: speechwiki/index.php/main_page Includes Ganglia Monitoring
101 Compute Pool
102 Compute Pool Main usage policy: Be nice to each other
103 Compute Pool Main usage policy: Be nice to each other If you want to start a job using more than 16 CPUs, please send to: speech-users@icsi.berkeley.edu.
104 Compute Pool Main usage policy: Be nice to each other If you want to start a job using more than 16 CPUs, please send to: speech-users@icsi.berkeley.edu. Never login into a compute machine to run jobs!
105 Compute Pool Main usage policy: Be nice to each other If you want to start a job using more than 16 CPUs, please send to: speech-users@icsi.berkeley.edu. Never login into a compute machine to run jobs! If in doubt or trouble: Send me .
106 ICSI Compute Pool: Basic Usage Easy compared to Amazon!
107 ICSI Compute Pool: Basic Usage showjobs -- Shows job currently running Easy compared to Amazon!
108 ICSI Compute Pool: Basic Usage showjobs -- Shows job currently running run-command -- Starts a job Easy compared to Amazon!
109 ICSI Compute Pool: Basic Usage showjobs -- Shows job currently running run-command -- Starts a job ssh machine /bin/kill pid -- Kill a job Easy compared to Amazon!
110 ICSI Compute Pool: Data Storage
111 ICSI Compute Pool: Data Storage Home/Project directory (backed up, strictly quota d)
112 ICSI Compute Pool: Data Storage Home/Project directory (backed up, strictly quota d) Local scratch space (fast, local machine, not backed up)
113 ICSI Compute Pool: Data Storage Home/Project directory (backed up, strictly quota d) Local scratch space (fast, local machine, not backed up) Networked scratch space (not backed up)
114 ICSI Compute Pool: Data Storage Home/Project directory (backed up, strictly quota d) Local scratch space (fast, local machine, not backed up) Networked scratch space (not backed up) Temporary space (so-called ttmp, networked, deleted automatically)
115 ICSI Compute Pool: Availabilty There are always other jobs...
116 Some Project Ideas (MediaEval Dataset)
117 Some Project Ideas (MediaEval Dataset) Build the any tag detector
118 Some Project Ideas (MediaEval Dataset) Build the any tag detector City-scale location estimation
119 Some Project Ideas (MediaEval Dataset) Build the any tag detector City-scale location estimation Rural/Non-Rural Detector
120 Some Project Ideas (MediaEval Dataset) Build the any tag detector City-scale location estimation Rural/Non-Rural Detector Correlate Users among videos
121 Some Project Ideas (MediaEval Dataset) Build the any tag detector City-scale location estimation Rural/Non-Rural Detector Correlate Users among videos Find videos from a certain country
122 Some Project Ideas (MediaEval Dataset) Build the any tag detector City-scale location estimation Rural/Non-Rural Detector Correlate Users among videos Find videos from a certain country Correlate video quality with tag quality
123 Some Project Ideas (TrecVid Dataset)
124 Some Project Ideas (TrecVid Dataset) Build a simple TrecVid MED system!
125 Some Project Ideas (TrecVid Dataset) Build a simple TrecVid MED system! Build a keyword-spotter
126 Some Project Ideas (TrecVid Dataset) Build a simple TrecVid MED system! Build a keyword-spotter Build a face detector
127 Some Project Ideas (TrecVid Dataset) Build a simple TrecVid MED system! Build a keyword-spotter Build a face detector Sort videos by visual similarity
128 Some Project Ideas (TrecVid Dataset) Build a simple TrecVid MED system! Build a keyword-spotter Build a face detector Sort videos by visual similarity
129 Some Project Ideas (TrecVid Dataset) Build a simple TrecVid MED system! Build a keyword-spotter Build a face detector Sort videos by visual similarity
130 Some Project Ideas (1M Song Dataset)
131 Some Project Ideas (1M Song Dataset) Build a dubbed-song recognition system for the TrecVid MED set
132 Some Project Ideas (1M Song Dataset) Build a dubbed-song recognition system for the TrecVid MED set Correlate song similarity with user ratings
133 Some Project Ideas (1M Song Dataset) Build a dubbed-song recognition system for the TrecVid MED set Correlate song similarity with user ratings Cluster and sort songs by tags and correlate with acoustic clustering
134 Some Project Ideas (1M Song Dataset) Build a dubbed-song recognition system for the TrecVid MED set Correlate song similarity with user ratings Cluster and sort songs by tags and correlate with acoustic clustering Try to align lyrics with songs
135 Some Project Ideas (1M Song Dataset) Build a dubbed-song recognition system for the TrecVid MED set Correlate song similarity with user ratings Cluster and sort songs by tags and correlate with acoustic clustering Try to align lyrics with songs
136 This Week (Lecture) Architectural Considerations for Large Scale Conten Analysis 38
137 Next Week (Project Meeting) Amazon EC2 and how to use it 39
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