This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
A Generic Scheme for Color Image Retrieval Based on the Multivariate Wald-Wolfowitz Test
June 2005 (vol. 17 no. 6)
pp. 808-819
In this study, a conceptually simple, yet flexible and extendable strategy to contrast two different color images is introduced. The proposed approach is based on the multivariate Wald-Wolfowitz test, a nonparametric test that assesses the commonality between two different sets of multivariate observations. It provides an aggregate gauge of the match between color images, taking into consideration all the (selected) low-level characteristics, while alleviating correspondence issues. We show that a powerful measure of similarity between two color images can emerge from the statistical comparison of their representations in a properly formed feature space. For the sake of simplicity, the RGB-space is selected as the feature space, while we are experimenting with different ways to represent the images within this space. By altering the feature-extraction implementation, complementary ways to portray the image content appear. The reported results, from the application on a diverse collection of images, clearly demonstrate the effectiveness of our method, its superiority over previous methods, and suggest that even further improvements can be achieved along the same line of research. It is not only the unifying character that makes our strategy appealing, but also the fact that the retrieval performance does not increase continuously with the amount of details in the image representation. The latter sets an upper limit to the computational demands and reminds of performance plateaus reached by novel approaches in information retrieval.

[1] M. Flickner, H. Sawhney, W. Niblack, J. Ashley, Q. Huang, B. Dom, M. Gorkani, J. Hafner, D. Lee, D. Petkovic, D. Steele, and P. Yanker, “Query by Image and Video Content: The QBIC System,” Computer, vol. 28, no. 9, pp. 23-32, Sept. 1995.
[2] A. Pentland, R.W. Ricard, and S. Sclaroff, “Photobook: Content-Based Manipulation of Image Databases,” Int'l J. Computer Vision, vol. 18, no. 3, pp. 233-255, 1996.
[3] J.R. Smith and S.F. Chang, “VisualSEEK: A Fully Automated Content-Based Image Query System,” Proc. 1996 ACM Int'l Multimedia Conf., pp. 87-98, 1996.
[4] W.Y. Ma and B.S. Manjunath, “Netra: A Toobox for Navigating Large Image Database,” Proc. IEEE Int'l Conf. Image Processing, vol. 1, pp. 568-571, Oct. 1997.
[5] C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Quering,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 8, pp. 1026-1038, Aug. 2002.
[6] E.G.M. Petrakis, C. Faloutsos, and K.-I. Lin, “ImageMap: An Image Indexing Method Based on Spatial Similarity,” IEEE Trans. Knowledge and Data Eng., vol. 14, no. 5, pp. 979-987, Sept./Oct. 2002.
[7] V. Mezaris, H. Doulaverakis, R. Medina, S. Herrmann, I. Kompatsiaris, and M.G. Strintzis, “A Test-Bed for Region-Based Image Retrieval Using Multiple Segmentation Algorithms and the MPEG-7 eXperimentation Model: The Schema Reference System,” Proc. Int'l Conf. Image and Video Retrieval (CIVR 2004), 2004.
[8] A. Natsev, R. Rastogi, and K. Shim, “WALRUS: A Similarity Retrieval Algorithm for Image Databases,” IEEE Trans. Knowledge and Data Eng., vol. 16, no. 3, pp. 301-316, Mar. 2004.
[9] B.S. Manjunath, P. Salembier, and T. Sikora, Introduction to MPEG-7: Multimedia Content Description Interface. New York: John Wiley & Sons, 2002.
[10] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval at the End of the Early Years,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
[11] Y. Rui, T.S. Huang, M. Ortega, and S. Mehrotra, “Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval,” IEEE Trans. Circuits and Systems for Video Technology, vol. 8, no. 5, pp. 644-655, 1998.
[12] E. Chang and S. Tong, “SVMActive— Support Vector Machine Active Learning for Image Retrieval,” UCSB Technical Report, Nov. 2001.
[13] R.C. Veltkamp, M. Tanase, and D. Sent, “Features in Content-Based Image Retrieval Systems: A Survey,” State-of-the-Art in Content-Based Image and Video Retrieval, R.C. Veltkamp, H. Burkhardt, and H.-P. Kriegel, eds., pp. 97-124, Kluwer, 2001.
[14] R. Schettini, G. Ciocca, and S. Zuffi, “A Survey on Methods for Colour Image Indexing and Retrieval in Image Databases,” Color Imaging Science: Exploiting Digital Media, R. Luo and L. MacDonald eds., J. Wiley, 2001.
[15] Y. Rubner, J. Puzicha, C. Tomasi, and J.M. Buhmann, “Empirical Evaluation of Dissimilarity Measures for Color and Texture,” Computer Vision and Image Understanding, vol. 84, pp. 25-43, 2001.
[16] M.J. Swain and D.H. Ballard, “Color Indexing,” Int'l J. Computer Vision, vol. 7, no. 1, pp. 11-32, 1991.
[17] V. Castelli and L.D. Bergman, Image Databases: Search and Retrieval of Digital Imagery. New York: John Wiley & Sons, 2002.
[18] Y. Gong, C.H. Chuan, and G. Xiaoyi, “Image Indexing and Retrieval Using Color Histograms,” Multimedia Tools and Application, vol. 2, pp. 133-156, 1996.
[19] M. Stricker and A. Dimai, “Color Indexing with Weak Spatial Constraints,” SPIE Proc., no. 2670, pp. 29-40, 1996.
[20] J.R. Smith and S.-F. Chang, “Single Color Extraction and Image Query,” Proc. IEEE Int'l Conf. Image Processing, Oct. 1995.
[21] G. Pass, R. Zabih, and J. Miller, “Comparing Images Using Color Coherence Vectors,” Proc. Fourth ACM Multimedia Conf., 1996.
[22] J. Huang, R. Kumar, S. Mitra, W. Zhu, and R. Zabih, “Image Indexing Using Color Correlogram,” Proc. IEEE Conf. Computer Vision Pattern Recognition, pp. 762-768, 1997.
[23] M. Stricker and M. Orengo, “Similarity of Color Images,” Proc. SPIE Storage and Retrieval for Image and Video Databases, pp. 381-392, 1995.
[24] G. Paschos, I. Radev, and N. Prabakar, “Image Content-Based Retrieval Using Chromaticity Moments,” IEEE Trans. Knowledge and Data Eng., vol. 15, no. 5, pp. 1069-1072, Sept./Oct. 2003.
[25] B.M. Mehtre, M.S. Kankanhalli, A.D. Narasimhalu, and G.C. Man, “Color Matching for Image Retrieval,” Pattern Recognition Letters, vol. 16, no. 3, pp. 325-331, 1995.
[26] T. Gevers and A.W. M. Smeulders, “Content-Based Image Retrieval by Viewpoint-Invariant Color Indexing,” Image and Vision Computing, vol. 17, no. 7, pp. 475-488, 1999.
[27] I.K. Park, I.D. Yun, and S.U. Lee, “Color Image Retrieval using Hybrid Graph Representation,” Image and Vision Computing, vol. 17, no. 7, pp. 465-474, 1999.
[28] T. Randen and J.H. Husoy, “Image Content Search by Color and Texture Properties,” Proc. Int'l Conf. Image Processing, pp. 580-583, 1997.
[29] J.H. Friedman and L.C. Rafsky, “Multivariate Generalizations of the Wald-Wolfowitz and Smirnov Two-Sample Tests,” Annals of Statistics, vol. 7, no. 4, pp. 697-717, July 1979.
[30] C.T. Zahn, “Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters,” IEEE Trans. Computers, vol. 20, no. 1, Jan. 1971.
[31] R.C. Prim, “Shortest Connection Networks and Some Generalizations,” Bell Systems Technical J., vol. 36, pp. 1389-1401, 1957.
[32] V. Gouet and N. Boujemaa, “Object-Based Queries Using Color Points of Interest,” Proc. IEEE Workshop Content-Based Access of Images and Videos (CBAIVL), Dec. 2001.
[33] B. Moghaddam, H. Biermann, and D. Margaritis, “Image Retrieval with Local and Spatial Queries,” Proc. IEEE Int'l Conf. Image Processing (ICIP), pp. 542-545, Sept. 2000.
[34] S.K. Thompson, Sampling. New York: John Wiley & Sons, 1992.
[35] J. Costa and A.O. Hero, “Geodesic Entropic Graphs for Dimension and Entropy Estimation in Manifold Learning,” IEEE Trans. Signal Processing, vol. 52, no. 8, pp. 2210-2221, 2004.
[36] Corel Stock Photo Library, Corel Corp., Ontario, Canada.
[37] Y. Rubner and C. Tomasi, Perceptual Metrics for Image Database Navigation. Kluwer Academic Publishers, 2001.
[38] J.B. Kruskal, “Multi-Dimensional Scaling by Optimizing Goodness-of-Fit to a Nonmetric Hypothesis,” Psychometrica, vol. 29, pp. 1-27, 1964.
[39] A.O. Hero, B. Ma, O.J.J. Michel, and J. Gorman, “Applications of Entropic Spanning Graphs,” IEEE Signal Processing Magazine, vol. 19, no. 5, pp. 85-95, 2002.
[40] R. Motwani and P. Raghavan, Randomized Algorithms. Cambridge Univ. Press, 2000.
[41] Ch. Theoharatos, N. Laskaris, G. Economou, and S. Fotopoulos, “A Similarity Measure for Color Image Retrieval and Indexing Based on the Multivariate Two Sample Problem,” Proc. EuropeanSignal Processing Conf. (EUSIPCO), 2004.
[42] E. Bingham and H. Mannila, “Random Projection in Dimensionality Reduction: Applications to Image and Text Data,” Proc. Seventh ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD-2001), pp. 245-250, 2001.
[43] K. Vu, K.A. Hua, and W. Tavanapong, “Image Retrieval Based on Regions of Interest,” IEEE Trans. Knowledge and Data Eng., vol. 15, no. 4, pp. 1045-1049, July/Aug. 2003.
[44] N. Laskaris and S. Fotopoulos, “A Novel Training Scheme for Neural-Network Based Vector Quantizers and Its Application in Image Compression,” Neurocomputing, vol. 61, pp. 421-427, 2004.

Index Terms:
Image retrieval, multivariate statistics, sampling, graph-theoretic methods, similarity measures, multivariate visualization.
Citation:
Christos Theoharatos, Nikolaos A. Laskaris, George Economou, Spiros Fotopoulos, "A Generic Scheme for Color Image Retrieval Based on the Multivariate Wald-Wolfowitz Test," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 808-819, June 2005, doi:10.1109/TKDE.2005.85
Usage of this product signifies your acceptance of the Terms of Use.