1. Content-Based Video Analysis based on Audiovisual Features for Knowledge Discovery Chia-Hung Yeh Signal and Image Processing Institute Department of Electrical Engineering University of Southern California

2. Vision

3. Guidelines n Motivation n Introduction n Overview of visual and audio content n Video abstraction n Multimodal information concept n Knowledge discovery via video mining n Our previous work n Conclusion and future work

4. Motivation n Amazing growth in the amount of digital video data in recent years. n Develop tools for classify, retrieve and abstract video content n Develop tools for summarization and abstraction n Bridge a gap between low-level features and high- level semantic content n To let machine understand video is important and challenging

5. Why, What and How n Why video content analysis? –Modern multimedia technologies have led to huge amount of digital video collections. But, efficient access to video content is still in its infancy, because of its bulky data volume and unstructured data format. n What is video content analysis? –Video content analysis analyzes the video content and attempts to automatically understand the embedded video semantics as humans do n How to do video content analysis?

6. Overview of Visual Content n Structured analysis –Extract hierarchical video structure Text Document Words segmented into Sentences Key sentences grouped into

7. Overview of Audio Content n Continuous in the time domain, not like visual n Multiple sound source exists in a sound track like many objects in a single frame n It is tough to separate audio content and give a suitable description n Framework in MPEG-7, silence, timbre, waveform, spectal, harmonic and fundamental frequency n Some special features for music and speech

8. Content-Based Video Indexing n Process of attaching content based labels to video shots n Essential for content-based classification and retrieval n Some required techniques –Shot detection –Key frame selection –Object segmentation and recognition –Visual/audio feature extraction –Speech recognition, video text, VOCR

9. Content-Based Video Classification n Segment & classify videos into meaning categories n Classify videos based on predefined topic n Multimodal concept –Visual features –Audio features –Metadata features n Domain-specific knowledge

10. Query (Retrieval Methods) n Simple visual feature query n Feature combination query n Query by example (QBE) –Retrieve video which is similar to example n Localized feature query –Example: retrieve video with a running car toward right n Object relationship query n Concept query (query by keyword) n Metadata –Time, date and etc.

11. The Ways to Browse a Video n Playback faster –Audio time scale modification – time saving factor 1.5 to 2.5 –15% - 20% time reduction by removing and shortening pauses n Storyboard –Composed of representative still frames (Keyframes) n Moving storyboard –Display keyframes while synchronized with the original audio track n Highlight –Pre-defined special event (example: sport and news) n Skimming –Extract short video clips to build a much shorter video

12. Timeline of Related Technique Development

13. Image Retrieval and Video Browsing n Query by Image Content (QBIC), IBM, 1995 –Complex multi-feature and multi-object queries n Video browsing –Quickly and efficiently Discover the information –Browsing and searching are usually complement each other –Visual content browsing us easier than audio content –Achieved by static storyboard, dynamic video clips, fast forward n Representative work –Gary Marchionini, University of Maryland –S.-F. Chang, Columbia University

14. Video Abstraction n Video summarization and video skimming –Belong to video abstraction and different from video browsing –Automatically retrieve the most significant and most representative a collection of segments n Required techniques –Shot detection, scene generation –Motion analysis –Face recognition –Audio segmentation –Text detection –Music detection

15. Video Abstraction n A video abstract –A sequence of still or moving images which preserve essential original video content while it is much shorter than the original one n Applications –Automated authoring of web content Web news Web seminar –Consumer domain applications Analyzing, filtering, and browsing

16. Video Summarization (I) n A collection of salient frames that represent the underlying content n Most related work focus on the ways to extract still frame n Categorize into three classes –Frame-based Randomly or uniformly select –Shot-based Keyframe –Feature-based Motion, color and so on

17. Video Summarization (II) n Representative work –Y. Taniguchi, (1995) Frame-based scheme Simple but may not representative due to not uniform length of shots –H.-J. Zhang, Microsoft Research China (1997) Keyframe based on color histogram –Gong and Liu, NEC Laboratories of American (2003) SVD (Single Value Decomposition) Capture temporal and spatial characteristics –Tseng, Lin and J. R. Smith, IBM T. J. Research Center (2002) Video summarization scheme for pervasive mobile device

18. Video Skimming n A good skim is much like a movie trailer n A synopsis of the entire video n Representative work –M. Smith and T. Kanade, Carnegie Mellon University (1995) Audio and image characterization –S. Pfeiffer, University of Mannheim (1996) VAbstract system Detection of special events such as dialogs, explosions and text occurrences –H. Sundaram and S.-F. Chang, Columbia University (2001) A semantics skimming system Visual complexity for human understanding Film syntax

19. Video Skimming – Application n Video content transcoding –Content-based live sport video filtering

20. Video Shot Structure n Shot, a cinematic term, is the smallest addressable video unit (the building block). A shot contains a set of continuously recorded frames n Two types of video shots: –Camera break  abrupt content change between neighboring frames. Usually corresponds to an editing cut –Gradual transition  smooth content change over a set of consecutive frames. Usually caused by special effects n Shot detection is usually the first step towards video content analysis

21. Scene Characteristics n Scene is a semantic concept which refers to a relatively complete video paragraph with coherent semantic meaning It is subjectively defined n Shots within a movie scene have following 3 features –Visual similarity  Since a scene could only be developed within certain spatial and temporal localities, the directors have to repeat some essential shots to convey parallelism and continuity of activities due to the sequential nature of film making –Audio similarity Similar background noises Speeches from the same person have similar acoustic characteristics –Time locality Visually similar shots should also be temporally close to each other if they do belong to the same scene

22. Basic Audio Features n Energy –Silence or pause detection n Zero crossing rate (ZCR) –The frequency of the audio signal amplitude passing through the zero value in a given time n Energy centroid –Speech range: 100 Hz to 7k Hz –Music range: 16 Hz to 16000 Hz n Band periodicity –Harmonic sounds –Music: High frequency components are integer multiples of the lowest one –Speech: Pitch n MFCC - (Mel-Frequency Cepstral Coefficients) –13 linearly-spaced filters

23. Multimodal Information Concept

24. Multimodal Framework for Video Content Interpretation n Application on automatic TV Programs abstraction n Allow user to request topic-level programs n Integrate multiple modalities: visual, audio and text information n Multi-level concepts –Low: low-level feature –Mid: object detection, event modeling –High: classification result of semantic content n Probabilistic model: using Bayesian network for classification (causal relationship, domain- knowledge)

25. Probabilistic Model – Data Fusion

26. How to Work with the Framework n Preprocessing –Video segmentation (shot detection) and key frame selection –VOCR, speech recognition n Feature Extraction –Visual features based on key-frame Color, texture, shape, sketch, etc. –Motion features Camera operation: Panning, Tilting, Zooming, Tracking, Booming, Dollying Motion trajectories (moving objects) Object abstraction, recognition –Audio features average energy, bandwidth, pitch, mel-frequency cepstral coefficients, etc. –Textual features (Transcript) Knowledge tree, a lot of keyword categories: politics, entertainment, stock, art, war, etc. Word spotting, vote histogram n Building and training the Bayesian network

27. Challenging Points n Preprocessing is significant in the framework. –Accuracy of key-frame selection –Accuracy of speech recognition & VOCR n Good feature extraction is important for the performance of classification. n Modeling semantic video objects and events n How to integrate multiple modalities still need to be well considered

28. Knowledge Discovery via Video Mining n Objectives –Find the hidden links between isolated news, events, etc. –Find the general trend of an event development –Predict the possible future event –Discover abnormal events n Required Technologies –Domain-specific knowledge model –Mining association rules, sequential patterns and correlations –Effective and fast classification and clustering n Challenges –Model build-up in special knowledge domain –Integration of semantic mining and feature-based mining –Effective and scalable classification and clustering algorithms

29. Video Mining Issues n Frequent/Sequential Pattern Discovery –Fast and scalable algorithms for mining frequent, sequential and structured patterns and for correlation analysis –Similarity of rule/event search/measurement n Efficient and fast classification and clustering algorithms –Constraint-based classification and clustering algorithms –Spatiotemporal data mining algorithms –Stream data mining (classification and clustering) algorithms n Surprise/outlier discovery and measurement –Detection of outliers based on similarity and trend analysis –Detection of outliers and surprised events based on stream data mining algorithms n Multidimensional data mining for trend prediction

30. Framework of Video Mining

31. Our Previous Work n TV Commercial Detection –Visual/audio information processing n Cinema rules –Intensity mapping n Tempo analysis in digital video (Professional video) –Audio tempo –Motion tempo n Home video processing (Non-professional) –Quality enhancement (Bad shot detection) –Music and video matching

32. Commercial Detection n First step to do any TV program content management n Monitor broadcast –Government –Advertisement Company n Commercial features –Delimiting black frame (not available in some countries) –High cut frequency and short shot interval (important feature) –Still images –Special editing styles and effects –Text and logo

33. Commercial Detection n Visual information processing –Black frame detection –Shot detection & its statistic analysis –Still image detection –Text-region detection –Edge change rate detection n Audio information processing –Volume control –Silence

34. Commercial Detection n Structure of TV program Normal program Normal program Normal Program with Station logo Spot Black frame Structure of TV program

35. Shot Detection & Its Statistic Analysis Commercial Start point

36. Still Image Detection n Still Image –Video Clip is composed of a sequence of image –Find out a set of consecutive images that have little change over a period of time Difficulty –Even though we feel that video clip is still, the difference between two consecutive images is seldom zero –It is tough to measure the moving part. (human eyes are sensitive to motion) n Main idea –Quantify motion in each image to detect still image

37. Still Image Detection Really still images Error detection

38. Tempo Analysis and Cinema Rules n The visual story - seeing the structure of film, TV, and new media, Bruce Block –Relationship between story structure and visual structure Their intensity maps are correlated –Principle of contrast and affinity The greater the contrast in a visual component, the more the visual intensity or dynamic increases

39. Cinema Rules Every feature film has a well designed story structure, which contains the beginning (exposition), the middle (conflict), and the end (resolution) R 0 120 110 1020... … EX CO CX Time length of the story in minutes Story Intensity 0 100 R 0 120 110 1020... … EX CO CX Time length of the story in minutes Story Intensity 0 100 EX: exposition  gives the facts needed to begin the story CO: conflict  contains rising actions or conflict CX: climax R: resolution  end the story

40. Cinema Rules n Scene: –A simple theme in a scene –Each scene is composed of setup part, progressing part, and resolution part –Final film is just a way to present this theme Dialog Close-up view n A story unit –A example of scene Main actors drove the main actress from train station back to home –A simple action Met at train station ->On the road->Another main actor joined them -> Arrive home

41. Audio Tempo n Music tempo n Definition in music –Note –Meter: A longer period contains many beats. For example, we can count as ONE-two-three, ONE-two-three –Tempo (pace/beat period) It is often indicated in the beginning. For example, the rate should be 100 quarter notes per minute (100 times we clap per minute)

42. Audio Tempo n Speech tempo –Emotion detection –Segmental durations Syllable or phoneme n Audio tempo –Short time pace Short-term memory –The number of sound events per unit of time The more events, the faster it seems to go –Onset A new note or a new syllable

43. Audio Tempo n Diagram of audio tempo analysis

44. Audio Tempo n Frequency filterbank –Perceptual frequency –Critical bands Wavelet-packet Multirate system n Envelope extractor –Rectify –Filtering: 50 ms half-Hamming window n Differentiator –First-order difference –Half-wave rectified Input signal and detected onsets

45. Audio Tempo n Boundary of story units –Local minima of audio tempo n Post signal processing –Help to get local minima –Three steps Lowpass filtering Morphological operation –Minmax –Close operation Detect local minima –Detected valleys Post processing for audio tempo analysis

46. Motion Analysis n The variance of motion vector –Where is a window, is the average length of motion vectors for each shot, and is shot index

47. Motion Analysis n Boundary of story units –Transition Edges n Post processing –Morphological operation Median Maxmin Minmax –Gradient –Detect edges Post processing for visual tempo

48. Skimming Video n Test data –Legends of The Fall Beginning 26 minutes MPEG format –352*240 pixels –44.1 KHz

49. Home Video Processing n Home video characteristics –Fragmental –Sound may not be very important –Bad shots Stabilization Focus Lighting Shooting tips 1Shoot lots of short scenes (5 ~ 10 seconds) 2 Use zoom in/out to take exposition shots or emphasize something 3Zoom or pan slowly 4Get a lot of face shots 5Keep a steady hand 6Make sure your subject is well lit

50. Bad Shots n Shaky –Drive –Walk n Vibration of the camera motions of successive frames

51. Bad Shots n Ill-light –Too dark/bright –Variance too much Diaphragm n Lighting Problem –Average of luminance Highest 1/3 pixels and lowest 1/3 pixels Negative feedback

52. Bad Shots n Blur –Motion blur –Out-of-focus blur –Foggy blur

53. Music and Video Matching n Shot detection n Remove bad shots n Match music tempo –Shot length –Motion activity

54. Authoring Scheme n Match music tempo –High tempo Small segment length –Transition time High motion activity

55. Experimental Results n Test data –Input music: 5.5- minutes music, Canon –Input video clips: Activities of babies of 0 ~ 3 years old Man-made bad shots Average clip length is about 20 seconds Total length is 50 minutes

56. Well-Known Research in Video Content Analysis Field n Well-known university –Digital Video Multimedia laboratory (DVMM), Columbia University –MIT Media laboratory –Information Digital Video Understanding, Carnegie Mellon University –Department of Electrical and Computer Engineering, University of Illinois of Urbana-Champaign –Signal and Image Processing Institute, University of Southern California –Department of Electrical Engineering, Princeton University –Language and media processing laboratory, University of Maryland

57. Well-Known Research in Video Content Analysis Field n Well-known R&D laboratory –IBM T. J. Watson research center –IBM Almaden research center –Intel corporation –Sharp Laboratory of America (SLA) –Microsoft research laboratory –Microsoft research China –Hawlett-Packard research laboratory –AT&T Bell laboratory –InterVideo –Pinnacle

58. Conclusion n Introduction of several basic concepts n Basic processing and low-level feature extraction n Semantic video modeling and indexing n Multimodal framework for topic classification of Video n Knowledge discovery via video mining n Our research results n Discussion of Challenging problems

59. Questions Thank You