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Fast retrieval of color objects with multidimensional orthogonal polynomials

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Abstract

In this paper, a color model for the Orthogonal Polynomials Transform and modifications to the generating function of the transform’s coefficients in order to enhance the speed of the transform have been proposed. Utilizing this simple and integer-based transform, a fast color image annotation and retrieval system is proposed. In the proposed retrieval system, images are represented as a mixture of Gaussians which are built from the transform’s partial coefficients. Annotation is performed by estimating the Kullback Leibler distance between the Gaussian distributions of the query and that of the database. This retrieval system is fast owing to the following reasons: 1) Usage of a computationally light transform 2) Sufficiency of partial decoding of the transform’s coefficients for building the image representation owing to its energy compaction property 3) Exploitation of the inherent symmetry of the point spread operator of transform which is useful for fast determination of the transform’s coefficients and 4) Non-usage of any time consuming weight assignment algorithm while fusing multiple features into the feature vector. The algorithm is validated on the COIL-100 database which has been categorized into six types for the purpose of analyzing the results better. An optimum number of extracted features and Gaussian mixtures that give a good annotation and retrieval performance is determined. The performance of the proposed system is compared with that of other recent compressed domain techniques and also with the feature set given by the local descriptors of SIFT.

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References

  • Agnihotri, L., Dimitrova, N., Soletic, M. (2002). Multi-layered videotext extraction method. In: Proceedings of the IEEE international conference on multimedia and expo, 2, 213–216.

  • Aksoy, S., & Haralick, R. M. (2000). Probabilistic vs. geometric similarity measures for image retrieval. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition, 2, 2357–2362.

  • Avrithis, Y. S., Doulamis, A. D., Doulais, N. D., & Kollias, S. D. (1999). A stochastic framework for optimal key frame extraction from MPEG video databases. Computer Vision Image Understanding, 75(1), 3–24.

    Article  Google Scholar 

  • Au, K. M., Law, N. F., & Siu, W. C. (2003). Unified feature analysis in different compressed domains. In: Proceedings of the fourth international conference on information, communications and signal processing, pp. 71–75.

  • Bae, H. J., & Jung, S. H. (1997). Image retrieval using texture based on DCT. In: Proceedings of the international conference on information and communications security, 2, 1065–1068.

  • Biernacki, C., Celeux, G., & Govaert, G. (2003). Choosing starting values for EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models. Computational Statistics and Data Analysis, 41, 561–575.

    Article  MATH  MathSciNet  Google Scholar 

  • Bosch, P., Van Ballegooij, A., deVries, A., & Kersten, M. (2001). Exact matching in image databases. In: Proceedings of the first IEEE international conference on multimedia and expo, pp. 513–516.

  • Carson, C., Thomas, M., Belongie, S., Hellerstein, J. M., & Malik, J. (1999). Blobworld: A system for region-based image indexing and retrieval. In: Proceedings of third international conference on visual information and information systems, pp. 509–516.

  • Carson, C., Belongie, S., Greenspan, H., & Malik, J. (2002). Blobworld: Image segmentation using expectation-maximization and its application to image querying. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(8), 1026–1038.

    Article  Google Scholar 

  • Chang, C., & Wu, T. C. (1995). An exact match retrieval scheme based upon principal component analysis. Pattern Recognition Letters, 16(5), 465–47.

    Article  MathSciNet  Google Scholar 

  • Chatzigiorgaki, M., Skodras, A. N. (2009). Real-time keyframe extraction towards video content identification. In: Proceedings of the 16th international conference on digital signal processing, pp. 1–6.

  • Chen, P. X., Feng, G. (2009). Image retrieval based on dominant color and texture features in DCT domain. In: Proceedings of Chinese conference on pattern recognition, pp. 1–5.

  • Choi, B., Han, S., Chung, B., Ryou, J. (2010). Design and performance evaluation of temporal motion and color energy features for objectionable video classification. In: Proceedings of the 6th international conference on advanced information management and service. pp. 37–41.

  • COIL100 database from http://www.cs.columbia.edu/CAVE/software/softlib/coil-100

  • Doulamis, N. D., Doulamis, A. D., Avrithis, Y. S., & Kolias, S. D. (1998). Video content representation using optimal extraction of frames and scenes. IEEE International Conference on Image Processing, 1, 875–879.

    Google Scholar 

  • Gali, R., Dewal, M. L., & Anand, R. S. (2012). Genetic algorithm for content based image retrieval. In: Proceedings of the fourth international conference on computational intelligence, communication systems and networks, pp. 243–247.

  • Greenspan, H., Goldberger, J., & Riddel, L. (2001). A continuous probabilistic framework for image matching. Journal of Computer Vision and Image Understanding, 84, 384–406.

    Article  MATH  Google Scholar 

  • Greenspan, H., Goldberger, J., Goron, S. (2003). An efficient image similarity measure based on approximations of KL-Divergence between two Gaussian mixtures, In: Proceedings of the ninth IEEE international conference on computer vision, 1, 487–493.

  • Guru, D. S., & Punitha, P. (2004). An invariant scheme for exact match retrieval of symbolic images based upon principal component analysis. Pattern Recognition Letters, 25(1), 73–86.

    Article  Google Scholar 

  • Han, Ju, & Ma, K. K. (2002). Fuzzy color histogram and its use in color image retrieval. IEEE Transactions On Image Processing, 11(8), 944–952.

    Article  Google Scholar 

  • Haoran, Y., Rajan, D., & Tien, C. L. (2003). An efficient video classification system based on HMM in compressed domain. In: Proceedings of the joint conference of the 4th international conference on information, communications and signal processing, and fourth pacific rim, Vol. 3, pp. 1546–1550.

  • Jalab, H. A. (2011). Image retrieval system based on color layout descriptor and Gabor filters. In: Proceedings of IEEE conference on open systems, pp. 32–36.

  • Jie, X. (2011). An audio-video mixed fingerprint generation algorithm based on key frames. In: Proceedings of the 13th IEEE international conference on communication technology, pp. 833–837.

  • Kekre, H. B., Sarode, M. T. K., & Sudeep, D. T. (2009). Image retrieval using color-texture features from DCT on VQ codevectors obtained by Kekre’s fast codebook generation. ICGST International Journal on Graphics, Vision and Image Processing (GVIP), 9(5), 1–9.

    Google Scholar 

  • Kim, Y. M., Choi, S. W., & Lee, S. W. (2000). Fast scene change detection using direct feature extraction from MPEG compressed videos. In: Proceedings of the 15th international conference on Pattern Recognition, 3, 174–177.

  • Krishnamoorthy, R. (2007). Transform coding of monochrome images with a statistical design of experiments approach to separate noise. Pattern Recognition Letters, 28, 771–777.

    Article  Google Scholar 

  • Krishnamoorthy, R., & Sheba Kezia Malarchelvi, P. D. (2008). Selective combinational encryption of gray scale images using orthogonal polynomials based transformation. IJCSNS International Journal of Computer Science and Network Security, 8(5), 195–204.

  • Krishnamoorthy, R., & Kannan, N. (2009). A new integer image coding technique based on orthogonal polynomials. Journal of Image Vision and Computing, 27(8), 999–1006.

    Article  Google Scholar 

  • Krishnamoorthy, R., & Kalpana, J. (2010). Indexing and retrieval of visually similar images in the orthogonal polynomials transform domain, to appear In: Proceedings of the second international conference on data engg. and management, LNCS, 6411, 196–203.

  • Krishnamoorthy, R., & Kalpana, J. (2011). Minimum distortion clustering technique for orthogonal polynomials transform vector quantizer. In: Proceedings of the international conference on communication, computing and security pp. 443–448.

  • Kullback, S. (1968). Information theory and statistics. New York: Dover Publications.

    Google Scholar 

  • Lowe, D. G. (2004). Distinctive image features from scale invariant key points. International Journal of Computer Vision, 60, 91–110.

    Article  Google Scholar 

  • Lay, J. A., & Guang, L. (1999). Image retrieval based on energy histograms of the low frequency DCT coefficients, In: Proceedings of the IEEE international conference on acoustics, speech, & signal processing, 6, 3009–3012.

  • Li, J., Wang, J. Z. (2006) Real-time computerized annotation of pictures. In: Proceedings of the 14th annual ACM international conference on multimedia, pp. 911–920.

  • Li, H., Liu, G., & Li, Y. (2002). An effective approach to edge classification from DCT domain. Proceedings of the International Conference on Image Processing, 1, 940–943.

    Article  Google Scholar 

  • Liapis, S., & Tziritas, G. (2004). Color and texture image retrieval using chromaticity histograms and wavelet frames. IEEE Transactions on Multimedia, 6, 676–686.

    Article  Google Scholar 

  • Malik, F., Baharudin, B. B., & Ullah, K. (2011). Efficient image retrieval based on quantized histogram texture features in DCT domain. In: Frontiers of information technology, pp. 84–94.

  • Permuter, H., Francos, J., & Jermyn, H. (2003). Gaussian mixture models of texture and color for image database retrieval. In: Proceedings of IEEE international conference on acoustics, speech, signal processing, Hong Kong, Vol. 3, pp. 569–572.

  • Poursistani, P., Nezamabadi-pour, H., Askari, Moghadam, R., & Saeed, M. (to appear). Image indexing and retrieval in JPEG compressed domain based on vector quantization, Mathematical and computer modeling. doi:10.1016/j.mcm.2011.11.064.

  • Redner, E., & Walker, H. (1984). Mixture densities, maximum likelihood and the EM algorithm. SIAM Review, 26(2), 195–239.

    Article  MATH  MathSciNet  Google Scholar 

  • Reeves, R., Kubik, K., Osberger, W. (1997). Texture characterization of compressed aerial images using DCT coefficients. In: Proceedings of SPIE: Storage and retrieval for image and video databases, 3022, pp. 398–407.

  • Shao, H., Zhang, J.-W., Cui, W. C., & Zhao, H. (2003). Automatic feature weight assignment based on genetic algorithm for image retrieval. In: Proceedings of the IEEE international conference on Robotics. Intelligent systems and signal processing, 2, 731–735.

  • Sim, D. G., Kim, H. K., & Park, R. H. (2001). Fast texture description and retrieval of DCT-based compressed images. Electronic Letters, 37(1), 18–19.

    Article  Google Scholar 

  • Smith, J. R., Chang, S. F. (1994). Transform features for texture classification and discrimination in large image databases. In: Proceedings of the IEEE international conference on image processing, 10(6) pp. 3407–3411.

  • Smith, J. R., Chang, S. F. (1996). VisualSEEk: A fully automated content-based image query system. In: Proceedings of the ACM international conference on multimedia, (pp. 87–98), Boston.

  • Vadivel, A., Majumdar, A. K., Sural, S. (2004). Characteristics of weighted feature vector in content-based image retrieval applications. In: Proceedings of IEEE international conference on intelligent sensing and information processing, pp. 127–132, India.

  • Vasconcelos, N. (2001). On the complexity of probabilistic image retrieval. In: Proceedings of the eighth international conference on computer vision, 2, pp. 400–407.

  • Vogel, J., & Schiele, B. (2007). Semantic modeling of natural scenes for content based image retrieval. International Journal of Computer Vision, 72(2), 133–157.

    Article  Google Scholar 

  • Wang, J., Li, J., & Wiederhold, G. (2001). SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Transaction on Pattern Analysis and Machine Intelligence, 23(9), 947–963.

    Article  Google Scholar 

  • Zhang, W., Tang, J., & Li, C. (2003). The extraction of image’s salient points for image retrieval. Pattern Recognition Letters, 21, 71–79.

    Google Scholar 

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Krishnamoorthy, R., Kalpana, J. Fast retrieval of color objects with multidimensional orthogonal polynomials. Multidim Syst Sign Process 25, 637–657 (2014). https://doi.org/10.1007/s11045-013-0222-y

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