1. 의미정보 해석 - 지식기반 시스템 응용 - 2006.11.21 최보윤 소프트컴퓨팅 연구실 연세대학교

2. Collaborative capturing and interpretation of interactions Y. Sumi, I. Sadanori, T. Matsuguchi, S. Fels, and K. Mase Pervasive 2004 Workshop on Memory and Sharing of Experiences, pp. 1-7, 2004.

3. Overview Introduction Capturing interactions by multiple sensors Related works Implementation Interpreting interactions Video summary Corpus viewer: Tool for analyzing interaction patterns conclusions

4. Introduction Interaction corpus –Action highlights Generate diary –Social protocols of human interactions Sensors –Video cameras, microphone and physiological sensors ID tags –LED tag: infrared LED –IR tracker: Infrared signal tracking device Position and identity

5. Capturing interactions by multiple sensors Recording natural interactions –Multiple presenters and visitors in an exhibition room Sensors & Humanoid robots –Wearable sensors, stationary sensors Monitoring humans Video camera, microphone, IR tracker –Recording robots’ behavior logs and the reactions of the humans which connect the robots Central data server –Getting the data from the sensors and humanoid robots

6. Related works Smart environment –Supporting humans in a room –The Smart rooms, Intelligent room, AwareHome, Kidsroom and EasyLiving –Recognition of human behavior and understanding of the human’s intention Wearable systems –Collecting personal daily activities –Intelligent recording system Video summary systems –The physical quantity of video data captured by fixed cameras

7. Exhibition room Implementation

8. IR tracker & LED tag Implementation

9. Interpreting interactions Define interaction primitives –Events –Significant intervals or moments of activites IR tracker and LED tag minInterval and maxInterval –minInterval: 5 sec –maxInterval Ubiquitous sensors: 10 sec Wearable sensors: 20 sec

10. Video summary Assumptions –User, Booth Co-occurences Video summarization

11. Corpus viewer: Tool for analyzing interaction patterns

12. conclusions Method to build an interaction corpus using multiple sensors Segment and interpret interactions from huge data Provide a video summary Help social scientists

13. Using context and similarity for face and location identification M. Davis, M. Smith, F. Stentiford, A. Bambidele, J. Canny, N. Good, S. King and R. Janakiraman Proceedings of the IS&T/SPIE 18th Annual Symposium on Electronic Imaging Science and Technology Internet Imaging VII, 2006.

14. Overview Introduction System Overview Content Analysis Experimental Data Experimental Design Evaluation Discussion and Results Conclusions & Future Work

15. Introduction New way for the unsolved image content recognition –Mobile media capture, context-sensing, programmable computation and networking in the form of the nearly ubiquitous cameraphone Cameraphone –Platform for multimedia computing –Combination with the analysis of automatically gathered contextual metadata and media content analysis Contextual metadata –Temporal –Spatial –Social –Face recognition and place recognition Precision of face recognition –PCA 40%, SFA 50% Precision of location recognition –Color histogram 30%, CVA 50%, contextual metadata and CVA 67%

16. System Overview MMM2 –Gathering data and metadata –Server application: store photo metadata and user profile information –Client application: run the client handset –MMM2 Context Logger University of Helsinki Location information, Bluetooth radio Detect new photos, display interface or web browser, upload MMM2 server –MMM2 website Select a region of a photo and associate a person’s name with this region Creation of Ground-Truth Dataset

17. Location Recognition Similarity measures –Pattern recognition problem Cognitive Visual Attention –Comparison of two image –Drawn the parts in common –No memory of data Training and Classification –A nearest neighbor classifier –Location classification Visual Sub-cluster Extraction –Many different photos at each location –Location class by several sub-clusters –Adding more exemplars Not guarantee improvements Color Histogram Techniques –Pixel color distributions –Simplest visual sub cluster example corresponding to an exemplar Content Analysis

18. Face Recognition & GPS PCA –Eigenface principle –Short training time –Best accuracy LDA+PCA –LDA: Multiple images training Bayesian MAP & ML –Maximum a posteriori (MAP), maximum likelihood (ML) –Difference or similarity between two photos SFA (Sparse Factor Analysis) – –Y: a vector of (partially) observed values, X: latent vector representing user preference, m: “model” predicting user behavior, N: noise function GPS Clustering –Suitable format –K-means and farthest first cluster Content Analysis

19. Experimental Data Face Recognition on Cameraphone Data –NIST FERET dataset Mugshot –Full frontal view –Head-and-shoulders –27,000 cameraphone potos 66user, 10 months Multiple people Real world Photographic Location Data –1209 images Nokia 7610 cameraphones 12 location, 30 cell identities Berkeley Campuss

20. Experimental Design Training gallery –Hand-labeled with the names –Min of distances between all images in the photo and training gallery image k SFA model –Training Contextual metadata and the face recognizer outputs Contextual metadata only –Evaluation Precision-recall plots for each of the computer vision algorithms –Time Training time: 2 minutes Training for the Bayesian classifiers: 7 hours PCA and LDA classifiers: less than 10 minutes Face recognition for 4 algorithms: less than 1 minute

21. Evaluation Location by Contextual Metadata –Distribution of metadata: 579 items, 12 location Location by Metadata and Vision

22. Face Identification Experimental Results Discussion and Results

23. Location Identification Experimental Results Histogram classifier, the CVA classifier and metadata classifier –Bad performance Metadata –Limit the errors with Cell ID –Specific place at certain times of the day and days of the week Discussion and Results Error Rate Increase Per Feature Removed

24. Conclusions & Future Work New approach to the automatic identification of human faces and location if mobile images Combination of attributes –Contextual metadata –Image processing Torso-matching Context-aware location recognition research