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Our approach to Principal Cast Detection
Our approach to Principal Cast Detection
Activity Curves for Golf
Activity Curves for Golf
Activity Curve for Soccer
Activity Curve for Soccer
41
41
PVR: Personal Video Recorder
PVR: Personal Video Recorder
PVR: Personal Video Recorder
PVR: Personal Video Recorder
Previously observed pattern: Extended segments of very low activity
Previously observed pattern: Extended segments of very low activity
Point Distance Matrix
Point Distance Matrix
Illustration: Segmenting Haiden Video
Illustration: Segmenting Haiden Video
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Video Indexing and Summarization using Combinations of the MPEG-7 Motion Activity Descriptor with other MPEG-7 audio-visual descriptors

содержание презентации «Video Indexing and Summarization using Combinations of the MPEG-7 Motion Activity Descriptor with other MPEG-7 audio-visual descriptors.ppt»
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1Video Indexing and Summarization using 33to surges and dips in motion activity
Combinations of the MPEG-7 Motion Activity (perceived motion) Thus, for a given
Descriptor with other MPEG-7 audio-visual sport, we can look for certain temporal
descriptors. Ajay Divakaran MERL - patterns of motion activity that would
Mitsubishi Electric Research Labs Murray indicate an interesting event In sports
Hill, NJ. highlights, the emphasis is on key-events
2Outline. Introduction MPEG-7 Standard and not on key-frames. 33.
Motivation for proposed techniques Video 34Motion Activity Curve. Shot Detection
Summarization using Motion Activity Audio not meaningful for our purpose Compute
Assisted Video Summarization Principal motion activity (avg. mag. Of mv’s) for
Cast Detection with MPEG-7 Audio Features each P-frame Smooth the values using a 10
Automatic generation of Sports Highlights point MA filter followed by a median
Target Applications Personal Video filter Quantize into binary levels of high
Recorder Demonstration Initial work on and low motion using threshold Low
Video Mining Conclusion. 2. threshold for Golf, High for Soccer. 34.
3Team. Yours Truly Kadir A. Peker – 35Activity Curves for Golf. 35.
Colleague and Ex-Doctoral Student 36Activity Curve for Soccer. 36.
Regunathan Radhakrishnan – Current 37Highlights extraction : Golf. Play
Doctoral Student Romain Cabasson – Summer consists of long stretches of low activity
Intern Ziyou Xiong – Summer Intern and interspersed with bursts of interesting
Current Collaborator Padma Akella – high activity Look for rising edges in the
Initial Demo designer and developer quantized motion activity curve
Pradubkiat Bouklee – Initial Software Concatenate ten second segments beginning
developer. 3. at each of the points of interest marked
4MPEG-7 Objectives. To develop a above The concatenation forms the desired
standard to identify and describe the summary. 37.
multimedia content Formal name: Multimedia 38Highlights Extraction: Soccer. Play
Content Description Interface Enable quick consists of long stretches of high
access to desired content whether local or activity Interesting events lead to
not. 4. non-trivial stops in play leading to a
5MPEG-7: Key Technologies and Scope. short stretch of low MA Thus we look for
Description consumption. Description falling edges followed by a non-trivially
Production. 5. long stretch of low motion activity We are
6MPEG-7 and other Standards. Rate. able to find the interesting events this
Functionality. Emphasis on Subjective way but have many false alarms With our
Representation. Emphasis on Semantic interface false alarms are easy to skip.
Conveyance. MPEG-2 Studio, DTV. Hybrid 38.
Content Interactive TV, Video 39Strengths and Limitations of Our
Conferencing. Indexing Retrieving Approach. The extraction is rapid and can
Browsing. MPEG-4 SNHC Object-Based. MPEG-7 be done in real time We use an adaptively
Descriptors. MPEG-1 H.263. JPEG JPEG-2000. computed threshold that is suited to the
Visualization. Abstract Representation content An interface such as ours helps
Virtual Reality. 6. skip false alarms easily There are too
7MPEG-7 framework. MPEG-7 standardizes: many false alarms. 39.
Descriptors (Ds): representations of 40Current Approach to Extraction of
features to describe various types of Soccer Highlights. 40.
features of multimedia information to 4141.
define the syntax and the semantics of 42Summary of Sports Highlights
each feature representation Description Generation. Motion Activity provides a
Schemes (DSs) to specify pre-defined quick way to generate sports highlights We
structures and semantics of descriptors use a different strategy with each sport
and their relationship Description The simplicity of the technique allows
Definition Language (DDL) to allow the real-time tuning of thresholds to modify
creation of new DSs and, possibly, Ds and highlights Interactive interfaces enable
to allows the extension and modification effective use. 42.
of existing DSs – XML MPEG-7 Schema. 7. 43PVR: Personal Video Recorder. With
8MPEG-7 Motion Activity Descriptor. Massive Amounts of Locally Stored Content,
Feature Extraction from Video Uncompressed Need to Locate & Customize Content
Domain Color Histograms - Zhang et al According to User. Local Storage. Feature
Motion Estimation - Kanade et al Extraction & MPEG-7 Indexing. Video
Compressed Domain DC Images - Yeo et al, Codec. Browsing & Summarization.
Kobla et al Motion Vector Based - Zhang et Enhanced User Interface. 43.
al Bit Allocation - Feng et al, Divakaran 44Blind Summarization – A Video Mining
et al. 8. Approach to Video Summarization. Ajay
9Motivation for Compressed Domain Divakaran and Kadir A. Peker Mitsubishi
Extraction. Compressed domain feature Electric Research Laboratories Murray
extraction is fast. Block-matched motion Hill, NJ.
vectors are sufficient for gross 45Content Mining. What is Data Mining?
description. Motion vector based It is the discovery of patterns and
calculation can be easily normalized relationships in data. Makes heavy use of
w.r.t. encoding parameters. 9. statistical learning techniques such as
10Motivation for Descriptor. Need to regression and classification Has been
capture “pace” or Intensity of activity successfully applied to numerical data
For example, draw distinction between Application to multimedia content is the
“High Action” segments such as chase next logical step Most applicable to
scenes. “Low Action” segments such as stored surveillance video and home video
talking heads Emphasize simple extraction since patterns are not known a priori
and matching Use Gross Motion Should enable anomalous event detection
Characteristics thus avoiding object leading to highlight generation Not
segmentation, tracking etc. Compressed applicable at first glance to consumer
domain extraction is important. 10. video. 45.
11Proposed Motion Activity Descriptor. 46Content Mining vs. Typical Data
Attributes of Motion Activity Descriptor Mining. Commonalities Large data sets.
Intensity/Magnitude - 3 bits Spatial Video is well known to produce huge
Characteristics - 16 bits Temporal volumes of data Amenable to statistical
Characteristics - 30 bits Directional analysis – Many of the machine learning
Characteristics - 3 bits. tools work well with both kinds of data as
12MPEG-7 Intensity of Motion Activity. can be seen in the literature and our
Expresses “pace” or Intensity of Action research as well Differences Number of
Uses scale of 1-5, very low - low - medium features not necessarily as large as
- high - very high Extracted by suitably conventional data mining data sets Size of
quantizing variance of motion vector dataset not necessarily as large as
magnitude Motion Vectors extracted from conventional data mining data sets Popular
compressed bitstream Successfully tested data mining techniques such as CART may
with subjectively constructed Ground not be directly applicable and may need
Truth. 12. modification In summary, new mining
13Video Summarization using Motion techniques that retain the basic
Activity. Video sequence V:{f1, f2, … fN} philosophy while customizing the details
set of temporally ordered frames Any will have to be developed. 46.
temporally ordered subset of V is a 47Summarization cast as a Content Mining
summary Previous work: Color dominant Problem. DVD “Auto-Summarization” mode
Cluster frames based on image similarity inspires “blind Summarization” Content
Select representative frames from Summarization can be cast as follows:
clusters. 13. Classify segments into common and uncommon
14Motion Activity as Summarizability. events without necessarily knowing the
Hypothesis: Motion activity measures domain Common patterns – what this video
intensity of motion hence it measures is about Rare patterns – possibly
change in the video Therefore it indicates interesting events May help to categorize
Summarizability Test of the Hypothesis video, detect style... The Summary is then
Examine relationship between Fidelity of a combination of common and rare events
Summary and motion activity Results show Can hybridize with domain-dependent
close correlation and motivate novel techniques. 47.
summarization strategy. 14. 48Data Mining Basics. Associations Time
15Fidelity of a Summary. 15. series similarity Sequential patterns
16Test of Hypothesis. Segment the test Clustering “How does region A and B
sequence into shots Use the first frame of differ”, “Any anomaly in A”, “What goes
each shot as its Key-Frame (KF) Compute with item x” Marketing, molecular biology,
the fidelity of each key-frame as etc. 48.
described Compute the motion activity of 49Associations. A set of items i1..im; a
each shot For each MPEG-7 motion activity set of transactions containing subset of
threshold Identify shots that have the items; a database of transactions: Rule X
same or lower motion activity Find the ? Y (X, Y items) : Support s: s% of
percentage p of shots with unacceptable transactions have X,Y together Confidence
fidelity (>0.2) Plot p vs the MPEG-7 c: c% of the time buying X implies buying
motion activity thresholds. 16. Y Improvement: Ratio of P(X,Y) to
17Motion Activity as a Measure of P(X)*P(Y) Find all rules with support,
Summarizability. 17. confidence and improvement larger than
18Conclusions from Experiment. The specified thresholds. Continuous-valued
percentage of shots with unacceptable extension exists. 49.
fidelity grows monotonically with motion 50Some Basic Aspects. Unsupervised
activity In other words, as motion learning Similar to clustering vs.
activity grows, the shots become classification Estimation of joint
increasingly difficult to summarize Hence, probability density Find values of
motion activity is a direct indicator of (i1,i2,…,in) where P(i1, i2,…,in) is high.
summarizability Question: Is the first 50.
frame the best choice as a key-frame? 18. 51Current Direction. As a starting
19Optimal Key-Frame Selection Using point, try to discover the temporal
Motion Activity. Summarizability is an patterns we used in detecting golf
indication of change in the shot The highlights Then generalize to patterns
cumulative motion activity is therefore an across multiple features Associations
indication of the cumulative change in the between changes, e.g. activity level
shot. 19. change, speaker change, scene change, etc.
20Optimal Key-Frame Extraction Using 51.
Motion Activity. 20. 52Previously observed pattern: Extended
21Comparison with Opt. Fidelity KF. Mot. segments of very low activity followed by
Activity. Ddsh First Frame. Ddsh proposed a jump in activity. Corresponds to a
KF. Number of Shots. Very Low. 0.0116. player preparing for a swing, then hitting
0.0080. 25. Low. 0.0197. 0.0110. 133. the ball and the camera following the
Medium. 0.0406. 0.0316. 73. High. 0.0950. ball. 52.
0.0576. 28. Very High. Overall avg. 53Time sequence mining. Find all similar
0.0430. 0.0216. 21. sub-sequences in a given time sequence
22Optimal Key-Frame Selection Based on E.g. motion activity of a video sequence
Cumulative Motion Activity. 22. Previous work mostly query of a given
23Audio Assisted Video Browsing: sub-sequence in a larger sequence. 53.
Motivation. Baseline MHL visual 54Mining for Temporal Patterns. Given a
summarization works well only when sequence S(i) and window size w, construct
semantic segment boundaries are well the set of all subsequences of size w:
defined Semantic segment boundaries cannot S(1:w), S(2:w+1), …, S(N-w+1:N) Find the
be located easily using visual features cross-distances between each pair and
alone Audio is a rich source of content cluster Problem: How can we search for
semantics Should use audio features to similar sub-sequences for different window
locate semantic segment boundaries. 23. sizes? 54.
24Past Work. Principal Cast 55Point Distance Matrix. Let the
Identification using Audio – Wang et al distance between two sub-sequences of size
Topic Detection using Speech Recog. – w be: The distance between two points is:
Hanjalic etc Semantic Scene Segmentation Then. 55.
using Audio – Sundaram et al Past work has 56Point Distance Matrix. xi-xi+w.
emphasized classification of audio into xj-xj+w. 56.
crisp categories We would like both a 57Advantages of Using Point Distance
crisp categorization and a feature vector Matrix. Search for diagonal lines of low
that allows softer classification point-distance Not limited to a given
Generalized Sound Recognition Framework – window size, look for the longest possible
Casey et al Casey’s work provides a rich diagonal line of low point-distance values
audio-semantic framework for our research. By allowing non diagonal lines and curves,
24. we can utilize “Time Warping” Matching of
25MPEG-7 Feature Extraction for sub-sequences of different lengths. 57.
Generalized Sound Recognition. 25. 58Multi-resolution Pattern Discovery.
26Our approach to Principal Cast Multi-resolution analysis: Smooth and
Detection. MPEG-7 Generalized Sound sub-sample time series (conventional
Recognition. State Duration Histograms. multiscale, e.g. wavelets) Analysis with
Our Enhancement. Principal Cast. 26. various window sizes, matching across
27Proposed Audio-Assisted Video Browsing different window sizes (our method
Framework. 27. automatically handles this). 58.
28Audio-Assisted Video Browsing 59Illustration: Segmenting Haiden Video.
Framework. 28. Repeating temporal patterns. 59.
29MHL application of Casey’s approach to 60Other Issues. Clustering segments
News Video Browsing. Classify the audio after finding similarities Extend to other
segments of the news video into speech and features, multiple dimensions Currently
non-speech categories in first pass using motion activity only Extend to
Classify the speech segments into male and multi-dimensional feature vectors (e.g.
female speech Using K-means clustering color histogram) Extend to multiple
find the “principal” speakers in each features, multiple modalities (e.g. video
category The occurrence of each of the + audio) Using a normalized Euclidean
principal speakers provides a natural distance measure Normalization based on
semantic boundary Apply baseline visual local variance of data. 60.
summarization technique to semantic 61Block-diagram of time-series mining.
segments obtained above There is thus a 61.
two-level summarization of the news video. 62Target Applications. Surveillance
29. Video Can detect unusual events through
30Clustering Results for Male Principal video mining in stored video Home Video
Cast. 30. Can use event detection and other pattern
31Results and Challenges. Moderate discovery to manage home video
accuracy so far. Results are thus Entertainment Quality Video Blind
promising but not satisfactory Lack of Summarization Genre Independent yet
noise robustness and content dependence of event-aware processing Content Management
training process represent major hurdle for Large Video Databases All of the above
Currently working on eliminating such at a very large scale. 62.
problems through extensive training 63Future Extension - Model Based
Feature extraction too complex – currently Matching. Use more sophisticated
investigating compressed domain audio statistical techniques to fuse label
feature extraction Also examining streams. 63.
alternative architectures that preserve 64Conclusion. System Features Unique,
basic spirit of framework. 31. simple and flexible summarization
32Automatic Extraction of Sports Integrated Player-Browser Enable rapid and
Highlights. Rapid Sports Highlights convenient browsing Video Summarization
extraction is critical Past work has made using Motion Activity as Summarizability
use of color, camera motion etc. MPEG-7 Audio-based principal cast detection
Motion Activity Descriptor is simple Can Audio-visual feature based sports
use it to extract high action segments for highlights extraction Further
example Should be useful in highlight Possibilities Refine Audio-assisted
extraction. 32. browsing Incorporate other visual features
33Essential Strategy. Sports are Video Mining. 64.
governed by a set of rules Key events lead
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Video Indexing and Summarization using Combinations of the MPEG-7 Motion Activity Descriptor with other MPEG-7 audio-visual descriptors

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