|9:20 - 9:30||Introduction|
|9:30 - 10:15||Jiri Matas , Czech Technical University Prague||Short-term Model-Free Causal Tracking - the VOT Challenge [abstract] [slides] [video]
The Visual Object Tracking Challenge is a benchmarking activity for a certain class of trackers. The talk will introduce the challenge, its semi-automatic selection and annotation of test data and the performance metrics. We will review the results for 2013 and 2014, summarize the lessons learned and discuss changes considered for 2015.
|10:15 - 11:00||Patrick Pérez, Technicolor||Face2Face: Learning Metrics to Compare Faces [slides] [video]|
|11:00 - 11:30||Coffee break|
|11:30 - 12:15||Andrew Zisserman, University of Oxford||Time is On My Side [abstract] [slides]
|12:15 - 13:00||Albert Gordo, Xerox Research Center Europe||Towards Text Understanding: Word Image Representation, Matching and Recognition [slides] [video]|
|13:00 - 14:00||Lunch|
|14:00 - 14:40||Yannis Avrithis, National Technical University of Athens||Image Retrieval, Vector Quantization and Nearest Neighbor Search [abstract] [slides] [video]
|14:40 - 15:20||Philippe-Henri Gosselin, ENSEA Cergy||Scalable Learning and Indexing for Retrieval in Large Image Databases [slides] [video]|
|15:20 - 15:50||The Fire-Id project|
|15:50 - 16:20||Bye Bye Coffee|
Frédéric Jurie will give a talk one day before the workshop on Wednesday, October 1st 2014 between 14:00 - 15:00 in salle Métivier at Inria Rennes. The subject of his talk is Histograms of Pattern Sets for Image Classification, Object Recognition and Image re-Ranking [abstract]
This talk will summarize the work we presented in two recent papers [a,b]. We have introduced a a novel image representation capturing feature dependencies through the mining of meaningful combinations of visual features. This representation leads to a compact and discriminative encoding of images that can be used for image classification, object detection, object recognition or even image re-ranking. The method relies on (i) multiple random projections of the input space followed by local binarization of projected histograms encoded as sets of items, and (ii) the representation of images as Histograms of Pattern Sets (HoPS). The approach is validated on four publicly available datasets (Daimler Pedestrian Classification, Oxford flowers Classification, KTH Texture Categorization, PASCAL VOC2007), allowing comparisons with many recent approaches. The proposed image representation reaches state-of-the-art performance on each of these datasets. We will also present an efficient framework for image re-reanking based on the same image representation.
[a] Histograms of Pattern Sets for Image Classification and Object Recognition Winn Voravuthikunchai, Bruno Cremilleux and Frederic Jurie, IEEE Conference on Computer Vision and Pattern Recognition, 2014.
[b] Image re-ranking based on statistics of frequent patterns, Winn Voravuthikunchai, Bruno Cremilleux and Frederic Jurie, ACM International Conference on Multimedia Retrieval, 2014.
Registration is not required for these presentations.
Email: Herve dot Jegou at inria dot fr
Email: Andrei dot Bursuc at inria dot fr