TREC 2003 Video Retrieval Evaluation Overview Coordinators: Alan - PowerPoint PPT Presentation

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So, who did what ? The approaches….

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Observations o Most techniques are based on frame-frame comparisons, some with sliding windows; o Comparisons are based on colour and on luminance, mostly; o Some use adaptive thresholding, some don’t; o Most operate on decoded video stream; o Some have special treatment of motion during GTs, of flashes, of camera wipes; o Performances are getting better; ��

Story segmentation and news typing o Identify the individual news items in a news show o New task in TRECVID, has been studied in ASR/IR community (TDT) o Hope to show the gain of using video features 1. Segmentation task n Identify story boundaries in CNN and ABC news shows n Ground truth based on TDT 2 annotations n Evaluation based on precision & recall, boundaries have to be within +/- 5 seconds interval around ground truth boundaries 2. News classification task n Annotate stories as either news or non-news n Evaluation based on percentage of correctly identified news story footage ��

8 Participating Groups Dublin City University (Irl) Fudan Univ. (China) IBM Research (US) KDDI (JP) National Univ. Singapore (Sing.) StreamSage (US) Univ. of Central Florida (US) Univ. of Iowa (US) ��

Story segmentation: recall and precision by condition � �(6� ��7�� 6� ��7�� 7�� (��(�� (8-.��

Story segmentation: recall and precision by system and condition (1-4) � $�% Conditions: �� 1: V+A 4 4 2 �� 2 4 1 2 2: V+A+ASR �"� 1 2 1 3: ASR �� #$$� 3 3 4: Other �� 1 �� 9%� �� 3 3 1 �-��'��/� �� 2 4 %�� 1 �� 2 %(��+� 3 1 3 4 �� 3 3 1 4 3 3 3 3 �� 3 ��

Segmentation, within system (F) 1 0,9 0,8 0,7 0,6 F (beta=1) AV AV+ASR 0,5 ASR 0,4 0,3 0,2 0,1 0 NUS IBM Fudan Iowa DCU StreamSage ��

Story classsification: news recall and precision by condition � �(6� ��7�� (6� ��7�� 7�� (��(�� (8-.�� 3��(:��(��+�: ��

Story classsification: news recall and precision by condition - zoomed � �(6� ��7�� 6� ��7�� 7�� (�� (��(�� (8-.�� 3��(:��(��+�: ��

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Story classifcation: news recall and precision by system and condition (1-4) zoomed � 2 �� 2 Conditions: �� 1 1 2 1 4 4 1: V+A 4 �� "� 2: V+A+ASR 1 2 1 3: ASR 1 �� 2 1 2 4: Other 9%� 2 1 2 2 �� 3 3 %�� 3 �� %(��+� 2 3 2 3 3 3 1 �� 3��(:��(��+�: ��

Classification, within system (F) 1 0,95 0,9 0,85 AV F value AV+ASR 0,8 ASR OTHER 0,75 0,7 0,65 0,6 NUS IBM Fudan Iowa ��

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Observations o Video provides strong clues for story segmentation and even more for classification, best runs are either type 1 or 2 o AV runs generally have a higher precision o Combination of AV and ASR gives a small gain for segmentation o Most approaches are generic o Are the combination methods optimal? o Are the ASR segmentation runs state of the art? ��

FE Task definition o Goal: Build benchmark for detection methods of high-level features o Secondary goal: feature-indexing can help search and navigation o New: common feature annotation n Helps (a.o.) to standardize training resources across sites n Category A: sites work with just the common development data and common annotations n Category B: sites work with just the common development data and any annotation set n Category C: other ��

FE evaluation o Each feature is assumed to be binary: absent or present for each shot o Find shots that contain a certain feature, rank them according to confidence measure, submit the top 2000 o Submissions are pooled o Evaluate performance quality by measuring the average precision of each feature detection method ��

10 Participating Groups Accenture Technology Laboratories (US) Carnegie Mellon Univ. (US) CLIPS-IMAG (FR) CWI Amsterdam / Univ. of Twente (NL) Fudan Univ. (China) IBM Research (US) Imperial College London (UK) Institut Eurecom (FR) Univ. of Central Florida (US) Univ. Oulu/VTT (FI) ��

17 Features 11. Indoors 12. News subject face – not a news show person 13. People – at least three humans 14. Building – walled structure with roof 15. Road 16. Vegetation – living vegetation in its natural env. 17. Animal 18. Female speech – woman speaking (visible, audible) 19. Car/truck/bus – exterior of .. ��

17 Features 20. Aircraft 21. News subject monologue – uninterrupted 22. Non-studio setting 23. Sporting event 24. Weather news 25. Zoom in 26. Physical violence – between people / objects 27. Madeleine Albright – visible ��

Who worked on which features e c h t s n n c w e e e l v e o e o n n e i p o v n e o X s i r c o g d l i e n a t n s a g m u n a e i t h l c r f e n i a l f t o t t m o a a s t i l e r s i s a s p m s d o o m r w g w e o y d n r o c d l p i i r h e a e W o e u n e r e o e a n S P v i Z P o a N N p b F a N 3��%$ �� c i r 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Accenture Technology Laboratories (US) X X X Carnegie Mellon Univ. (US) X X X X X X X X X X X X X X X X X CLIPS-IMAG (FR) X CWI Amsterdam / Univ. of Twente (NL) X X X X X X X X X X X X X X Fudan Univ. (China) X X X X X X X X X X X X X X X X X IBM Research (US) X X X X X X X X X X X X X X X X X Imperial College London (UK) X Institut Eurecom (FR) X X X X X X X X X X X X X X X Univ. of Central Florida (US) X X Univ. Oulu/VTT (FI) X X X X X X X X X X X X X X X 6 6 6 6 6 7 6 6 6 6 4 7 6 8 3 6 6 ��

AvgP by feature (all runs) Middle half of the data Median ��

AvgP by feature (top 10 runs) �� 1 �� 2 �&��/�(4�� 3 �� 4 5 �� 6 7 �� 8 9 �� 10 Median �� Median -> � �� -��

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