Abstract
In this paper we present a novel, compact and effective method for extracting texture information from an image. We denominate this method as Motif Correlogram (MC), which computes the correlation between motif pairs of the same type. The proposed method was evaluated using different metrics commonly used in image retrieval, such as ARP (Average Retrieval Precision), ARR (Average Retrieval Rate) and ANMRR (Average Normalized Retrieval Rank). Also, the proposed scheme was compared with other texture descriptors, such as Steerable FIltres, Edge Histogram Descriptor (EHD) and two Co-occurrence Matrix-based algorithms: Motif Co-Occurrence Matrix (MCM) and Directional Local Motif XoR Patterns (DLMXoRP). The performance of the proposed method was evaluated using the Kylberg Dataset. The evaluation results show the proposed texture descriptor improves the texture image retrieval performance.
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References
Xu, F., Zhang, Y.-J.: Evaluation and comparison of texture descriptors proposed in MPEG-7. J. Vis. Commun. Image R 17, 701–716 (2006)
Wang, X.-Y., Yu, Y.-J., Yang, H.-Y.: An effective image retrieval scheme using color, texture and shape features. Comput. Stand. Interfaces 33, 59–68 (2011)
Milind, V.L., Praveen, B., Pritesh, J.: An efective content-based image retrieval using color, texture and shape feature. In: Mohapatra, D.P., Patnaik, S. (eds.) Intelligent Computing, Networking and Informatics. AISC, vol. 243, pp. 1163–1170. Springer, India (2014)
Park, D.K., Jeon, S.Y., Won, C.S.: Efficient use of local edge histogram descriptor. ETRI J. 24, 23–30 (2002)
Jhanwar, N., Chaudhuri, S., Seetharaman, G., Zavidovique, B.: Content-based image retrieval using motif co-occurrence matrix. Image Vis. Comput. 22, 1211–1220 (2004)
Vipparthi, S.K., Nagar, S.K.: Expert image retrieval system using directional local motif XoR patterns. Expert Syst. Appl. 41, 8016–8026 (2014)
Huang, J., Kumar, S.R., Mitra, M., Wei-Jing, Z., Zabih, R.: Image indexing using color correlograms. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 762–768 (1997)
Manjunath, B.S., Ohm, J.-R., Vasudevan, V.V., Yamada, A.: Color and texture descriptors. IEEE Trans. on Circuit Syst. Video Technol. 24, 345–360 (2001)
Ahmed, T., Massudi, M., Husniza, H., Loay, G.: A weighted dominant color descriptor for content-based image retrieval. J. Vis. Commun. Image Represent. 24, 345–360 (2013)
Kylberg, G.: The Kylberg Texture Dataset v. 1.0. Centre for Image Analysis, Swedish University of Agricultural Science and Uppsala University, External report (Blue Series) No. 35. http://www.cb.uu.se/~gustaf/texture/
Zhang, D., Lu, G.: Evaluation of similarity measurement for image retrieval. In: IEEE International Conference on Neural Networks and Signal Processing, pp. 14–17 (2003)
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Fierro-Radilla, A.N., Calderon-Auza, G., Nakano-Miyatake, M., Pérez-Meana, H.M. (2015). Motif Correlogram for Texture Image Retrieval. In: Fujita, H., Guizzi, G. (eds) Intelligent Software Methodologies, Tools and Techniques. SoMeT 2015. Communications in Computer and Information Science, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-319-22689-7_38
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DOI: https://doi.org/10.1007/978-3-319-22689-7_38
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