Context-Based Conceptual Image Indexing

Automatic semantic classification of image databases is very useful for users searching and browsing but it is at the same time a very challenging research problem. Local features based image classification is one of the promising way to bridge the semantic gap in detecting concepts. This paper proposes a framework for incorporating contextual information into the concept detection process. The proposed method combines local and global classifiers (SVMs) with stacking. We studied the impact of topologic and semantic contexts in concept detection performance and proposed solutions to handle the large amount of dimensions involved in classified data. We conducted experiments on TRECVID'04 data set with 48104 images and 5 concepts. We found that the use of context yields a significant improvement both for the topologic and semantic contexts

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

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

[3]  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..

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

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

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

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

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

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

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

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

[12]  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.

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

[14]  Dennis Koelma,et al.  The MediaMill TRECVID 2008 Semantic Video Search Engine , 2008, TRECVID.