CLIPS-LSR-NII Experiments at TRECVID 2005

This paper presents the systems used by CLIPSIMAG laboratory. We participated to shot segmentation and high-level extraction tasks. We focus this year on High-Level Features Extraction task, based on key frames classification. We propose an original and promising framework for incorporating contextual information (from image content) into the concept detection process. The proposed method combines local and global classifiers with stacking, using SVM. We handle topologic and semantic contexts in concept detection performance and proposed solutions to handle the large amount of dimensions involved in classified data.

[1]  Matthew B. Blaschko,et al.  Combining Local and Global Image Features for Object Class Recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[2]  Jiebo Luo,et al.  Probabilistic spatial context models for scene content understanding , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  Nicu Sebe,et al.  Boosting contextual information in content-based image retrieval , 2004, MIR '04.

[4]  Stéphane Ayache,et al.  Context-Based Conceptual Image Indexing , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[5]  Matthieu Cord,et al.  A comparison of active classification methods for content-based image retrieval , 2004, CVDB '04.

[6]  Pietro Perona,et al.  Mutual Boosting for Contextual Inference , 2003, NIPS.

[7]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[8]  Thomas S. Huang,et al.  Fusion of global and local information for object detection , 2002, Object recognition supported by user interaction for service robots.

[9]  Stefan M. Rüger,et al.  Evaluation of Texture Features for Content-Based Image Retrieval , 2004, CIVR.

[10]  Milind R. Naphade.  On supervision and statistical learning for semantic multimedia analysis , 2004, J. Vis. Commun. Image Represent..

[11]  Nando de Freitas,et al.  A Statistical Model for General Contextual Object Recognition , 2004, ECCV.

[12]  Harriet J. Nock,et al.  Discriminative model fusion for semantic concept detection and annotation in video , 2003, ACM Multimedia.

[13]  Antonio Torralba,et al.  Contextual Models for Object Detection Using Boosted Random Fields , 2004, NIPS.

[14]  Antonio Torralba,et al.  Object Detection and Localization Using Local and Global Features , 2006, Toward Category-Level Object Recognition.