Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

We present a new framework for characterizing and retrieving objects in cluttered scenes. Objects are best represented by characterizing both their parts and the mutual spatial relations among them. This CBIR system is based on a new representation describing every object taking into account the local properties of its parts and their mutual spatial relations, without relying on accurate segmentation. For this purpose, a new multi-dimensional histogram is used that measures the joint distribution of local properties and relative spatial positions. Instead of using a single descriptor for all the image, we represent the image by a set of histograms covering the object from different perspectives. We integrate this representation in a whole framework which has two stages. The first one is to allow an efficient retrieval based on the geometric properties (shape) of objects in images with clutter. This is achieved by i) using a contextual descriptor that incorporates the distribution of local structures, and ii) taking a proper distance that disregards the clutter of the images. At a second stage, we introduce a more discriminative descriptor that characterizes the parts of the objects by their color and their local tructure. By sing relevant-feedback and boosting as a feature selection algorithm, the system is able to learn simultaneously the information that characterize each part of the object along with their mutual spatial relations. Results are reported on two known databases and are quantitatively compared to other successful approaches

REFERENCES

• 1: Serge J. Belongie , Jitendra Malik , Jan Puzicha, Shape Matching and Object Recognition Using Shape Contexts, University of California at Berkeley, Berkeley, CA, 2002
• 2: Christopher J. C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, v.2 n.2, p.121-167, June 1998 [doi>10.1023/A:1009715923555]
• 3: Chad Carson , Megan Thomas , Serge Belongie , Joseph M. Hellerstein , Jitendra Malik, Blobworld: A System for Region-Based Image Indexing and Retrieval, Proceedings of the Third International Conference on Visual Information and Information Systems, p.509-516, June 02-04, 1999
• 4: Yixin Chen , James Z. Wang, A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.24 n.9, p.1252-1267, September 2002 [doi>10.1109/TPAMI.2002.1033216]
• 5: R.Fergus P.Perona and A.Zisserman. Object class recognition byunsupervised scale-invariant learning. In CVPR 2003.
• 6: Y.Freund and R.E.Shapire.Experiments with a new boosting algorithm.In International Conference on Machine Learning pages 148--156 1996.
• 7: T.Gevers and A.Smeulders.Pictoseek: Combining color and shape invariant features for image retrieval.IEEE Tran. IP 9(1):102--119 January2000.
• 8: J.D.H.D.Tagare C.C.Jaffe. Medical image databases:A content-based retrieval approach. Journal of the American Medical Informatics Association 4:184--198 1997.
• 9: Pengyu Hong , Thomas S. Huang, Spatial pattern discovery by learning a probabilistic parametric model from multiple attributed relational graphs, Discrete Applied Mathematics, v.139 n.1-3, p.113-135, 30 April 2004 [doi>10.1016/j.dam.2002.11.007]
• 10: N.Howe.A closer look at boosted image retrieval. In CIVR pages 61--70 2003.
• 11: Jing Huang , S. Ravi Kumar , Mandar Mitra , Wei-Jing Zhu , Ramin Zabih, Image Indexing Using Color Correlograms, Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97), p.762, June 17-19, 1997
• 12: Euripides G. M. Petrakis , Christos Faloutsos, Similarity Searching in Medical Image Databases, IEEE Transactions on Knowledge and Data Engineering, v.9 n.3, p.435-447, May 1997 [doi>10.1109/69.599932]
• 13: A.Rao R.Srihari and Z.Zhang.Geometric histogram:A distribution of geometric configurations of color subsets.In SPIE: Internet Imaging volume 3964 pages 91--101 2000.
• 14: Simone Santini , Ramesh Jain , Amarnath Gupta, A User Interface for Emergent Sementics in Image Databases, Proceedings of the IFIP TC2/WG2.6 Eighth Working Conference on Database Semantics- Semantic Issues in Multimedia Systems, p.123-143, January 04-08, 1999
• 15: Kaleem Siddiqi , Benjamin B. Kimia, Parts of Visual Form: Computational Aspects, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.17 n.3, p.239-251, March 1995 [doi>10.1109/34.368189]
• 16: Arnold W. M. Smeulders , Marcel Worring , Simone Santini , Amarnath Gupta , Ramesh Jain, Content-Based Image Retrieval at the End of the Early Years, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.22 n.12, p.1349-1380, December 2000 [doi>10.1109/34.895972]
• 17: Kinh Tieu , Paul Viola, Boosting Image Retrieval, International Journal of Computer Vision, v.56 n.1-2, p.17-36, January-February 2004 [doi>10.1023/B:VISI.0000004830.93820.78]
• 18: James Z. Wang , Jia Li , Gio Wiederhold, SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.23 n.9, p.947-963, September 2001 [doi>10.1109/34.955109]
• 19: Cao and W.Grosky. Negotiating the semantic gap:From feature maps to semantic landscapes. Pattern Recognition 35:593--600 2002.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms

Keywords:
boosting, content-based image retrieval oject recognition, contextual information