Distributional Distances in Color Image Retrieval with GMVQ-Generated Histograms
We investigate and compare the performance of several distributional distances in generic color image retrieval with an emphasis on symmetry and boundedness of the distances. Two histogram generation methods based on Gauss mixture vector quantization (GMVQ) are compared using Kullback-Leibler divergence (KLD). The joint histogram method shows a better retrieval performance than the Bayesian retrieval with the label histograms of interleaved data. A variety of distance measures are tested and compared for the joint histogram features produced by GMVQ, including an important set of Ali-Silvey distances, the Bhattacharyya distance, and a few other divergence measures based on Shannon entropy. Experimental results show that the Bhattacharyya distance and the L divergence are better than the histogram intersection (HI), but the KLD is poorer than the HI. In all cases, the symmetric version of a distance performs better than the asymmetric one and usually the bounded version of a distance gives better retrieval performance than the corresponding non-bounded.
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- 2.Atae-Allah, C., Gomez-Lopera, J., Luque-Escamilla, P., Martinez-Aroza, J., Roman-Roldan, R.: Image segmentation by Jensen-Shannon divergence. application to measurement of interfacial tension. In: IEEE International Conference on Pattern Recognition, Barcelona, Spain (September 2000)Google Scholar
- 3.Comaniciu, D., Meer, P., Xu, K., Tyler, D.: Retrieval performance improvement through low rank corrections. In: Workshop in Content-based Access to Image and Video Libraries, Fort Collins, Colorado (April 1999)Google Scholar
- 5.Jeong, S., Gray, R.M.: A comparison of EM and GMVQ in estimating Gauss mixtures: Application to probabilistic image retrieval. In: IEEE ICASSP, Philadelphia, PA (March 2005)Google Scholar
- 6.Jeong, S., Gray, R.M.: Minimum distortion color image retrieval based on Lloyd-clustered Gauss mixtures. In: Data Compression Conference (DCC), Snowbird, Utah (March 2005)Google Scholar
- 7.Jeong, S., Won, C.S., Gray, R.M.: Histogram-based image retrieval using Gauss mixture vector quantization. In: Proceedings of IEEE ICASSP, Hong Kong, China (April 2003)Google Scholar
- 8.Jeong, S., Won, C.S., Gray, R.M.: Image retrieval using color histograms generated by Gauss mixture vector quantization. Computer Vision and Image Understanding: Special Issue on Color for Image Indexing and Retrieval 94(1–3), 44–66 (2004)Google Scholar
- 9.Kailath, T.: The divergence and Bhattacharyya distance measures in signal selection. IEEE Transactions on Communication Technology 15(1) (February 1967)Google Scholar
- 10.Lazarevic, A., Pokrajac, D., Megalooikonomou, V., Obradovic, Z.: Distinguishing among 3-d distributions for brain image data classification. In: 4th International Conference Neural Networks and Expert Systems in Medicine and Healthcare, Milos Island, Greece (June 2001)Google Scholar
- 11.Lin, J.: Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory 37(1) (January 1991)Google Scholar
- 14.Vasconcelos, N.: Bayesian Models for Visual Information Retrieval. Phd thesis, Massachusetts Institute of Technology (June 2000)Google Scholar