Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.15, No.6 pp. 687-697, 2011
 C. Carson, S. Belongie, H. Greenspan, and J. Malik, “Blobworld: image segmentation using expectation-maximization and its application to image querying,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.24, No.8, pp. 1026-1038, 2002.
 R. d. S. Torres, A. X. Falcão, M. A. Gonçalves, J. P. Papa, B. Zhang, W. Fan, and E. A. Fox, “A genetic programming framework for content-based image retrieval,” Pattern Recognition, Vol.42, No.2, pp. 283-292, 2009.
 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, 1995.
 A. Pentland, R. W. Picard, and S. Sclaroff, “Photobook: contentbased manipulation of image databases,” Int. J. of Computer Vision, Vol.18, No.3, pp. 233-254, 1996.
 Z. Stejić, Y. Takama, and K. Hirota, “Relevance feedback-based image retrieval interface incorporating region and feature saliency patterns as visualizable image similarity criteria,” IEEE Trans. on Industrial Electronics, Vol.50, No.5, pp. 839-852, 2003.
 Z. Stejić, Y. Takama, and K. Hirota, “Mathematical aggregation operators in image retrieval: effect on retrieval performance and role in relevance feedback,” Signal Processing, Vol.85, No.2, pp. 297-324, 2005.
 J. Z. Wang, J. Li, and G. Wiederhold, “SIMPLIcity: semanticssensitive integrated matching for picture libraries,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.23, No.9, pp. 947-963, 2001.
 D. Geman, S. Geman, C. Graffigne, and P. Dong, “Boundary detection by constrained optimization,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol.12, No.7, pp. 609-628, 1990.
 P. Howarth and S. Rüger, “Fractional distance measures for contentbased image retrieval,” In The 27th European Conf. on IR Research, LNCS 3408, pp. 447-456, 2005.
 T. Kailath, “The divergence and Bhattacharyya distance measures in signal selection,” IEEE Trans. on Communication Technology, com-15, Vol.1, pp. 52-60, 1967.
 M. Kokare, B. N. Chatterji, and P. K. Biswas, “Comparison of similarity metrics for texture image retrieval,” In Conf. on Convergent Technologies for Asia-Pacific Region, Vol.2, pp. 571-575, 2003.
 J. Puzicha, T. Hofmann, and J. M. Buhmann, “Non-parametric similarity measures for unsupervised texture segmentation and image retrieval,” In IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR’97), pp. 267-272, 1997.
 M. J. Swain and D. H. Ballard, “Color indexing,” Int. J. of Computer Vision, Vol.7, No.1, pp. 11-32, 1991.
 D. Zhang and G. Lu, “Evaluation of similarity measurement for image retrieval,” In The 2003 Int. Conf. on Neural Networks and Signal Processing, Vol.2, pp. 928-931, 2003.
 H. Liu, D. Song, S. Rüger, R. Hu, and V. Uren, “Comparing dissimilarity measures for content-based image retrieval,” In The 4th Asian Information Retrieval Symposium on Information Retrieval Technology, LNCS 4993, pp. 44-50, 2008.
 K. Okamoto, F. Dong, S. Yoshida, and K. Hirota, “Content-based image retrieval via ranking of similarity measures,” In The 2010 Int. Symposium on Intelligent Systems, 2010.
 G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” In Workshop on Statistical Learning in Computer Vision (ECCV 2004), pp. 1-22, 2004.
 G. Ding, J. Wang, and K. Qin, “A visual word weighting scheme based on emerging itemsets for video annotation,” Information Processing Letters, Vol.110, No.16, pp. 692-696, 2010.
 Y. G. Jiang and C. W. Ngo, “Visual word proximity and linguistics for semantic video indexing and near-duplicate retrieval,” Computer Vision and Image Understanding, Vol.113, No.3, pp. 405-414, 2009.
 J. Sivic and A. Zisserman, “Video Google: A text retrieval approach to object matching in videos,” In The 9th IEEE Int. Conf. on Computer Vision, Vol.2, pp. 1470-1477, 2003.
 G. J. Burghouts and J. M. Geusebroek, “Performance evaluation of local colour invariants,” Computer Vision and Image Understanding, Vol.113, No.1, pp. 48-62, 2009.
 D. Arthur and S. Vassilvitskii, “k-means++: The advantages of careful seeding,” In Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027-1035, 2007.
 W. Zhang, T. Yoshida, and X. Tang, “A comparative study of TF*IDF, LSI and multi-words for text classification,” Expert Systems with Applications, Vol.38, No.3, pp. 2758-2765, 2011.
 K. Järvelin and J. Kekäläinen, “Cumulated gain-based evaluation of IR techniques,” ACM Trans. on Information Systems, Vol.20, No.4, pp. 422-446, 2002.
 A. Moffat and J. Zobel, “Rank-biased precision for measurement of retrieval effectiveness,” ACM Trans. on Information Systems, Vol.27, No.1, pp. 1-27, 2008.
 E. M. Voorhees and D. M. Tice, “The TREC-8 question answering track evaluation,” In Text Retrieval Conf. TREC-8, pp. 83-105, 1999.
 F. Wilcoxon, “Individual comparisons by ranking methods,” Biometrics Bulletin, Vol.1, No.6, pp. 80-83, 1945.
 M. Mizumoto, “Pictorial representations of fuzzy connectives, Part I: cases of t-norms, t-conorms and averaging operators,” Fuzzy Sets and Systems, Vol.31, No.2, pp. 217-242, 1989.
 K. M. Donald and A. F. Smeaton, “A comparison of score, rank and probability-based fusion methods for video shot retrieval,” In The 4th Int. Conf. on Image and Video Retrieval, LNCS 3568, pp. 61-70, 2010.
 C. J. v. Rijsbergen, “Information retrieval,” Department of Computing Science University of Glasgow, 1979.
 T. Sakai and N. Kando, “On information retrieval metrics designed for evaluation with incomplete relevance assessments,” Information Retrieval, Vol.11, No.5, pp. 447-470, 2008.
 Y. Rubner, C. Tomasi, and L. J. Guibas, “The Earth mover’s distance as a metric for image retrieval,” Int. J. of Computer Vision, Vol.40, No.2, pp. 99-121, 2000.
 D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. of Computer Vision, Vol.60, No.2, pp. 91-110, 2004.
 H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “SURF: speeded up robust features,” Computer Vision and Image Understanding, Vol.110, No.3, pp. 346-359, 2008.
 J. Puzicha, J. M. Buhmann, Y. Rubner, and C. Tomasi, “Empirical evaluation of dissimilarity measures for color and texture,” In The 7th IEEE Int. Conf. on Computer Vision, Vol.2, pp. 1165-1172, 1999.