MESH participation to TRECVID2008 HLFE

A group of four organizations from the MESH consortium (www.mesh-ip.eu) participated this year for the first time in the High Level Feature Extraction track in TRECVID. The partners were Telefonica I+D (TID, Spain), Informatics & Telematics Institute (ITI, Greece), National Technical University of Athens (NTUA, Greece) and Universidad Autonoma de Madrid (UAM, Spain). We submitted a total of 6 runs, using different variations and configurations over a common model. With only one exception, results obtained by those runs were below expectations, mostly due (we believe) to some implementation bugs discovered afterwards. Some of those errors have already been solved and we hope to correct the rest and improve the performance of the system for future editions.

[1]  Luc Van Gool,et al.  The 2005 PASCAL Visual Object Classes Challenge , 2005, MLCW.

[2]  Paul Over,et al.  Evaluation campaigns and TRECVid , 2006, MIR '06.

[3]  Stéphane Ayache,et al.  Video Corpus Annotation Using Active Learning , 2008, ECIR.

[4]  B. S. Manjunath,et al.  Introduction to MPEG-7: Multimedia Content Description Interface , 2002 .

[5]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[6]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Yannis Avrithis,et al.  A Region Thesaurus Approach for High-Level Concept Detection in the Natural Disaster Domain , 2007, SAMT.

[8]  L. Luo,et al.  A Comparison of Strategies for Unbalance Sample Distribution in Support Vector Machine , 2006, 2006 1ST IEEE Conference on Industrial Electronics and Applications.

[9]  Emine Yilmaz,et al.  Estimating average precision with incomplete and imperfect judgments , 2006, CIKM '06.

[10]  Chong-Wah Ngo,et al.  Evaluating bag-of-visual-words representations in scene classification , 2007, MIR '07.

[11]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[14]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[15]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Yannis Avrithis,et al.  Using region semantics and visual context for scene classification , 2008, 2008 15th IEEE International Conference on Image Processing.

[17]  Frédéric Jurie,et al.  Sampling Strategies for Bag-of-Features Image Classification , 2006, ECCV.

[18]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  Yannis Avrithis,et al.  Regions of interest for accurate object detection , 2008, 2008 International Workshop on Content-Based Multimedia Indexing.

[20]  Christian Petersohn Fraunhofer HHI at TRECVID 2004: Shot Boundary Detection System , 2004, TRECVID.