1. RIAO 2004 2 video retrieval systems

2. The Físchlár-News-Stories System: Personalised Access to an Archive of TV News Alan F. Smeaton, Cathal Gurrin, Howon Lee, Kieran McDonald, Noel Murphy, Noel E. O’Connor, David Wilson Centre for Digital Video Processing, Dublin City University Derry O’Sullivan, Barry Smyth Smart Media Institute, Department of Computer Science, University College Dublin

3. Introduction Físchlár systems –A family of tools for capturing, analysis, indexing, browsing, searching and summarisation of digital video information –Físchlár-News-Stories Provides access to a growing archive of broadcast TV news Segment news into shots and stories, calendar lookup, text search, link between related stories, personalisation and recommend stories

4. System overview

5. Shot boundary detection Shot –A single camera motion in time –We can have camera movement as well as object motion Shot cut –Hard cut –Gradual transition (GT) Boundary detection (Browne, et al., 2000) –Frame-frame similarity over a window of frames –Evaluation: TRECVID 2001 Over 90% precision and recall for hard cuts Somewhat less for GT

6. Story segmentation Cluster all keyframes of shots –Similarity: colour and edge histograms (O’Connor, et al., 2001) Anchorperson shots –One of the clusters will have an average keyframe- keyframe similarity much higher than the others and this will most likely be a cluster of anchorperson shots Beginning of news, beginning/end of advertisement –Apply a speech-music discrimination algorithm to the audio

7. Story segmentation Detect individual advertisements –Sadlier, et al., 2002 Shot length –Outside broadcasts an d footage video tends to have shorter shot lengths than the in-studio broadcasts Using SVM to determine story bounds –Combine the output of these analyses Evaluation (TRECVID 2003) –31% recall and 45% precision For the present time, the automatic segmentation is manually checked for accuracy every day

8. Search based on text Closed captions –Typing error, omit phrases or sentences –Time lagging Retrieval –Simple IR engine –When a story’s detail is displayed, we use the closed caption text from that story as a query against the closed caption archive and display summaries of the 10 top-ranked stories

9. Personalisation User feedback –Rating on a given news story using a 5-point scale –These ratings are used as input to a collaborative filtering system which can recommend news stories to users based on ratings from other users Need to recommend on new content User vs. stories ratings matrix is very sparse –Story-story similarity + user-story ratings

12. CIMWOS: A Multimedia Retrieval System based on Combined Text, Speech and Image Processing Harris Papageorgiou 1, Prokopis Prokopidis 1,2, Iason Demiros 1,2, Nikos Hatzigeorgiou 1, George Carayannis 1,2 1 Institute for Language and Speech Processing 2 National Technical University of Athens

13. Introduction CIMWOS –Multimedia, multimodal and multilingual –Content-based indexing, archiving, retrieval and on- demand delivery of audiovisual content –Video library Sports, broadcast news and documentaries in English, French and Greek –Combine speech, language and image understanding technology –Producing XML metadata annotations following the MPEG-7 standard

15. Speech processing Subsystem Speaker Change Detection (SCD) Automatic Speech Recognition (ASR) Speaker Identification (SID) Speaker Clustering (SC)

16. Text processing Subsystem Named Entity Detection (NED) Term Extraction (TE) Story Segmentation (SD) Topic Detection (TD) Speech Transcriptions Named Entity Detection Term Extraction Story Segmentation Topic Detection XML

17. Text processing Subsystem Applied on the textual data produced by the Speech Processing Subsystem Named entity detection –sentence boundary identification –POS tagging –NED Lookup modules that match lists of NEs and trigger-words against the text, hand-crafted and automatically generated pattern grammars, maximum entropy modeling, HMM models, decision-tree techniques, SVM classifier, etc. –Term extraction Identify single or multi-word indicative keywords Linguistic processing is performed through an augmented term grammar, the results of which are statistically filtered using frequency-based scores

18. Text processing Subsystem Story detection and topic classification –Employ the same set of models –Generative, mixture-based HMM One state per topic, one state modeling general language, that is words not specific to any topic Each state models a distribution of words given the particular topic Running the resulting models on a sliding window, thereby noting the change in topic-specific words as the window moves on

19. Image processing Subsystem Automatic Video Segmentation (AVS) –Shotcut detection and keyframe extraction –Measurement of differences between consecutive frames –Adaptive thresholding on motion and texture cues Face Detection (FD) and Identification (FI) –Locate faces in video sequences, and associate these faces with names –Based on SVM

20. Image processing Subsystem Object Recognition (OR) –The object’s surface is decomposed in a large number of regions –The spatial and temporal relationships of these regions are acquired from several example views Video Text Detection and Recognition (TDR) –OCR Text detection Text verification Text segmentation OCR

21. Integration All processing modules in the corresponding three modalities converge to a textual XML metadata annotation scheme following the MPEG-7 descriptors These XML metadata annotations are further processed, merged and loaded into CIMWOS Multimedia database

22. Indexing and retrieval Weighted Boolean model –Weight of index term: tf*idf –Image processing metadata are not weighted Two-step –1. Boolean exact-match Objects, topics and faces –2. Query best-match Text, terms and named entities Basic retrieval unit –passage

23. Indexing schema episode Story(ies) Passage(s) Shot(s) Subshot(s) Key-frame Face(s)Object(s) Text(s) Named entity(ies) Term(s) Word(s) Speaker(s) Topic(s)

24. Evaluation Greek news broadcasts –35.5 hours Collection A: 15 news, 18 hours –Segmentation, named entity identification, term extraction, retrieval Collection B: 15 news, 17 hours –retrieval 3 users –Gold annotations on the videos

25. Evaluation Segmentation –Precision: 89.94% –Recall: 86.7% –F-measure: 88.29 Term extraction –Precision: 34.8% –Recall: 60.28%

26. Evaluation Named entity identification

27. Evaluation Retrieval –Users translate each topic to queries –5 queries for each topic in average –Collection B Segmentation is based on stories –60% filter Filter out results that scored less 60% in the CIMWOS DB ranking system

28. Evaluation PrecisionRecallF-measure Collection A34.7557.7543.39 Collection A + 60% filter45.7853.5249.35 Collection B44.7850.2447.36 Collection B + 60% filter64.9637.0747.20