Image Similarity: A Genetic Algorithm Based Approach

Authors: R. C. Joshi, Shashikala Tapaswi

Abstract:

The paper proposes an approach using genetic algorithm for computing the region based image similarity. The image is denoted using a set of segmented regions reflecting color and texture properties of an image. An image is associated with a family of image features corresponding to the regions. The resemblance of two images is then defined as the overall similarity between two families of image features, and quantified by a similarity measure, which integrates properties of all the regions in the images. A genetic algorithm is applied to decide the most plausible matching. The performance of the proposed method is illustrated using examples from an image database of general-purpose images, and is shown to produce good results.

Keywords: Image Features, color descriptor, segmented classes, texture descriptors, genetic algorithm.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061587

References:

[1] H. Bandemer and W.Nather, "Fuzzy Data Analysis," Kluwer Academic Publishers, 1992.
[2] C.Carson, M. Thomas, S. Belongie, J. Hellerstein, J. Malik, "Blobworld: A System for Region-based Image Indexing and Retrieval," Proceedings of the 3rd Intn-l Conference on Visual Information Systems, 1999.
[3] C.Burges, "A Tutorial on Support Vector Machines for Pattern Recognition," Data Mining and Knowledge Discovery, 2(2), 1998.
[4] C.Bouman, M. Shapiro, "A Multiscale Random Field Model for Bayesian Image Segmentation," IEEE Transactions on Image Processing, 3(2): 162-177, 1994.
[5] N.Kingsbury, "Shift Invariant Properties of the Dual-Tree Complex Wavelet Transform," Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing, Phoenix, Arizona, USA, 1999.
[6] A.H.Kam,T.T.Ng, N.G.Kingsbury and W.J.Fitzgerald : "Content based image retrieval through object extraction and querying" ,Proc. IEEE Workshop on Content-based Access of Image and Video Libraries, Hilton Head Island, S Carolina, June 12, 2000.
[7] K.Fukunaga and L. Hosteler, "The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition," IEEE Transactions on Information Theory, 21:32-40, 1975.
[8] J. Smith, "Integrated Spatial and Feature Image Systems: Retrieval, Compression and Analysis," Ph.D thesis, Columbia University, USA, February, 1997.
[9] X. Zhang and B. Wandell, "Color Image Fidelity Metrics Evaluated Using Image Distortion Maps," Signal Processing, 70:201-214, 1998.
[10] J.Li, J.Z.Wang and G.Wiederhold, "IRM: Integrated region matching for image retrieval," Proc 8Th ACM Int-l Conference on Multimedia, pp.147-156, October 2000.
[11] D.P.Huttenlocher, G.A.Klanderman, W.J.Rucklidge, "Comparing images using the Hausdroff distance", Transaction on Pattern Analysis and Machine Intelligence, v.15, pp.850-863, 1999.
[12] J.T.Alander, "An Indexed Bibliography of Genetic Algorithms in Pattern Recognition", Report 94-1-PATTERN, University of Vaasa, 2001. URL :ftp://ftp.uwaasa.fi/cs/report94-1/.