On “shapes” of colors for content-based image retrieval

ABSTRACT

Color is a commonly used feature for realizing content-based image retrieval (CBIR). Towards this goal, this paper presents a new approach for CBIR which is based on well known and widely used color histograms. Contrasting to previous approaches, such as using a single color histogram for the whole image, or local color histograms for a fixed number of image cells, the one we propose (named Color Shape) uses a variable number of histograms, depending only on the actual number of colors present in the image. Our experiments using a large set of heterogeneous images and pre-defined query/answer sets show that the Color Shape approach offers good retrieval quality with relatively low space overhead, outperforming previous approaches.

REFERENCES

• 1: A.R. Appas, A.M. Darwish, A.I. El-Desouki, and S.I. Shaheen. Image indexing using composite regional color channels features. In Proc. of SPIE - Storage and Retrievalfor Image and Video Databases VII, volume 3656, pages 492-500, 1999.
• 2: J. Ashley, R. barber, M. Flickner, J. Hafner, D. Lee, W. Niblack, and D. Petkovic. Automatic and semi-automatic methods for image annotation and retrieval in qbic. In Proc. of SPIE - Storage and Retrievalfor Image and Video Databases III, volume 2420, pages 24-35, 1995.
• 3: Alberto Del Bimbo, Visual information retrieval, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1999
• 4: Paolo Ciaccia , Marco Patella , Pavel Zezula, M-tree: An Efficient Access Method for Similarity Search in Metric Spaces, Proceedings of the 23rd International Conference on Very Large Data Bases, p.426-435, August 25-29, 1997
• 5: A. Dimai. Spatial encoding using differences of global features. In Proc. of SPIE - Storage and Retrievalfor Image and Video Databases IV, volume 3022, pages 352-360, 1997.
• 6: C. Faloutsos , R. Barber , M. Flickner , J. Hafner , W. Niblack , D. Petkovic , W. Equitz, Efficient and effective querying by image content, Journal of Intelligent Information Systems, v.3 n.3-4, p.231-262, July 1994 [doi>10.1007/BF00962238]
• 7: Brian V. Funt , Graham D. Finlayson, Color Constant Color Indexing, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.17 n.5, p.522-529, May 1995 [doi>10.1109/34.391390]
• 8: L.J. Guibas, B. Rogoff, and C. Tomasi. Fixed-window image descriptors for image retrieval. In Proc. of SPIE - Storage and Retrievalfor Image and Video Databases III, volume 2420, pages 352-362, 1995.
• 9: Jamal Malki , Nozha Boujemaa , Chahab Nastar , Alexandre Winter, Region Queries without Segmentation for Image Retrieval by Content, Proceedings of the Third International Conference on Visual Information and Information Systems, p.115-122, June 02-04, 1999
• 10: Eugenio Di Sciascio , G. Mingolla , Marina Mongiello, Content-Based Image Retrieval over the Web Using Query by Sketch and Relevance Feedback, Proceedings of the Third International Conference on Visual Information and Information Systems, p.123-130, June 02-04, 1999
• 11: Nicu Sebe , Michael S. Lew , Dionysius P. Huijsmans, Multi-scale sub-image search, Proceedings of the seventh ACM international conference on Multimedia (Part 2), p.79-82, October 30-November 05, 1999, Orlando, Florida, United States [doi>10.1145/319878.319901]
• 12: Michael J. Swain , Dana H. Ballard, Color indexing, International Journal of Computer Vision, v.7 n.1, p.11-32, Nov. 1991 [doi>10.1007/BF00130487]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)

Additional Classification:
  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS

  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Image databases
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.7 Three-Dimensional Graphics and Realism
          Subjects: Color, shading, shadowing, and texture
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Similarity measures
  I.6 SIMULATION AND MODELING

General Terms:
Design, Experimentation, Management, Measurement, Performance, Theory

Keywords:
histograms, image databases, image metadata, image similarity retrieval