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Multimedia Retrieval. Outline Audio Retrieval Spoken information Music Document Image Analysis and Retrieval Video Retrieval.

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Presentation on theme: "Multimedia Retrieval. Outline Audio Retrieval Spoken information Music Document Image Analysis and Retrieval Video Retrieval."— Presentation transcript:

1 Multimedia Retrieval

2 Outline Audio Retrieval Spoken information Music Document Image Analysis and Retrieval Video Retrieval

3 A Taxonomy of Audio Sound MusicOther?Speech Classical Country DiscoHip Hop Jazz Rock Sports Announcer Female Male Orchestra String Quartet Choir Piano ?

4 Spoken Document Retrieval

5

6 Acoustic Modeling Describes the sounds that make up speech Lexicon Describes which sequences of speech sounds make up valid words Language Model Describes the likelihood of various sequences of words being spoken Speech Recognition Speech Recognition Knowledge Sources

7 Speech Recognition in Brief Pronunciation Lexicon Signal Processing Phonetic Probability Estimator (Acoustic Model) Decoder (Language Model) Words Speech Grammar

8 Hints For Better Recognition Topical information News of the day Image information ? Goal: improve the estimation p(word|acoustic_sig) Main idea: p(word|acoustic_sign)  p(word|acoustic_signal, X) What could be X?

9 Hints For Better Recognition Topical information News of the day Image information Lip reading Video Optical Character Recognition (VOCR) Goal: improve the estimation p(word|acoustic_sig) Main idea: p(word|acoustic_sign)  p(word|acoustic_signal, X) What could be X?

10 Speech Recognition Accuracy Word Error Rate

11 Information Retrieval Precision vs. Speech Accuracy Word Error Rate % of Text IR Relative Precision Indexing and Search of Multimodal Information, Hauptmann, A., Wactlar, H. Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP-97), Munich, Germany, April A rather small degradation in retrieval when word error rate is small than 30%

12 Spoken Document Retrieval Segmentation issue Continuous speech data without story boundaries Typical segmentation approaches ­Overlapping windows (30 sec for each segment) ­Automatic detection of speaker changes

13 Spoken Document Retrieval: Document Expansion Motivation: documents are erroneous Goal: apply expansion techniques to reduce the impacts of recognition errors in spoken documents Similar to query expansion

14 Spoken Document Retrieval: Document Expansion Motivation: documents are erroneous Goal: apply expansion techniques to reduce the impacts of recognition errors in spoken documents Similar to query expansion Clean Doc Collection (web docs) Speech Recognized Transcript doc1 doc2 doc3 doc4 Find common words in top ranked docs

15 Spoken Document Retrieval: Document Expansion Motivation: documents are erroneous Goal: apply expansion techniques to reduce the impacts of recognition errors in spoken documents Similar to query expansion Treat each speech document as a query Find clean documents that are relevant to speech documents Expand each speech document with the common words in the top ranked clean documents.

16 Document Expansion (Sighal & Piereira, 1999)

17 A Taxonomy of Audio Sound MusicOther?Speech Classical Country DiscoHip Hop Jazz Rock Sports Announcer Female Male Orchestra String Quartet Choir Piano ?

18 Music Information Retrieval

19 Music Retrieval A textual retrieval approach Using meta data: titles, artists, genres, … Content-based music retrieval Query by audio Query by score document/segment

20 Content-based Music Retrieval Short-term Autocorrelation Note Segmentation Mid-level Representation Similarity Comparison Query results (Ranked song list) Songs Database Midi message Extraction Microphone Signal input Sampling 11KHz Center Clipping Off-line processing On-line processing (Midi representation)

21 Content-based Music Retrieval  :  : N-gram representation  1 1 2C C –2C310 0 –2 0C C501 A vector representation for each music document A typical information retrieval problem

22 Document Image Analysis and Retrieval

23 Document Image Analysis Recognize text (OCR) convert page images to Unicode machine-printed, handwritten Analyze page layout geometry a 2-D problem (unlike speech, text) good ‘language-free’ algorithms Capture logical structure output marked-up text (XML, etc) exploit non-textual clues

24 Video/Image OCR Block Diagram Text Area Detection Text Area Preprocessing Commercial OCR Video or Image UTF8 Text

25 Text Detection

26 Low resolution (as low as 10 pixel height/character) limited by NTSC (352x248) /PAL/SECAM TV standard Complex background Character Hue and Brightness similar to background Video OCR

27 VOCR Preprocessing Problems

28 Video Frames (1/2 s intervals) Filtered FramesAND-ed Frames

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30 OCR Document Retrieval Task: find OCR recognized document relevant to a information need Challenge: erroneous documents  needs to handle with word errors

31 OCR Document Retrieval Correction based approaches Find potential word errors and replace each with the most likely correct one Partial matching approaches Word  a set of n-grams Word matches  n-gram matches

32 Video Retrieval

33 Video Retrieval - Application of Diverse Technologies Speech understanding for automatically derived transcripts Image understanding for video “paragraphing”; face, text and other object recognition Natural language for query expansion, topic detection and content summarization Human computer interaction for video display, navigation and reuse Integration overcomes limitation of each

34 Introduction to TREC Video Retrieval Track NIST TREC Video Track web site: nlpir.nist.gov/projects/trecvid/ Video Retrieval Track started in 2001 Investigation of content-based retrieval from digital video Focus on the shot as the unit of information retrieval rather than the scene or story/segment/clip

35 The TRECVID Collections hours, 74 queries, 8000 shots hours, 25 queries, shots Video from the Internet Archive between the ‘50’s and ’70’s Advertising, educational, industrial and amateur films Common shot boundaries 2003 – 56 hours, 25 queries, shots 1998 Broadcast News (CNN, ABC, CSpan) + Common Speech Recognition + Common Annotations 2004 – 61 hours, 24 queries, shots More 1998 Broadcast News

36 Sample Query and Target Query: Find pictures of Harry Hertz, Director of the National Quality Program, NIST Speech: We’re looking for people that have a broad range of expertise that have business knowledge that have knowledge on quality management on quality improvement and in particular … OCR: H,arry Hertz a Director aro 7 wa-,i,,ty Program,Harry Hertz a Director

37 System Architecture (Trec Video Track 2001) Combine video, audio and text retrieval scores Query TextImageAudio Text ScoreImage ScoreAudio Score Retrieval Agents Final Score

38 ARRRecall ASR Transcripts1.84%13.2% VOCR5.93%7.52% Image Retrieval14.99%24.45% Combine18.9%28.25% Results for TREC01


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