Abstract
Content-based image retrieval (CBIR) would be an important future trend in search engines. This paper proposed a nearest neighbor search (NNS) method that uses k-means clustering and pre-calculated distances on a known set of image samples to be used for performing image queries within the set. The proposed algorithm adds a clustering step prior to the rest on an existing algorithm and uses the nearest clusters only for the NNS. The distance between the query images to the cluster is determined by using twice the standard deviation for the clusters to estimate the boundary of each cluster. The feature used is grey-level co-occurrence matrices (GLCM). This reduces both the samples explored by 25.21% and execution time by 26.62% for 16 chosen clusters within 23 clusters and a search radius of 0.2. The experimental results had shown an improvement in time complexity but on the same time sacrifices the hit rate that had dropped from 100% in the previous method that explores all potential samples but the proposed method only manage to achieve 70.77%.
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References
Niblack, W., Barber, R., Equitz, W., Flickner, M., Glasman, E., Pektovic, D., Yanker, P., Faloutsos, C., Taubin, G.: The QBIC project: querying images by content using color, texture, and shape. In: Storage and Retrieval for Image and Video Databases, San Jose, CA (1993)
Yoshitaka, A., Ichikawa, T.: A Survey on Content-Based Retrieval for Multimedia Databases. IEEE Transactions on Knowledge and Data Engineering 11(1), 81–93 (1999)
Veltkamp, R.C., Tanase, M.: Content-Based Image Retrieval Systems: A Survey. Technical Report UU-CS-2000-34 (2000)
Google Goggles, http://www.google.com/mobile/goggles/
Tou, J.Y., Tay, Y.H., Lau, P.Y.: Exploiting Pre-Calculated Distance In Nearest Neighbour Search on Query Images For CBIR. In: Proceedings International Workshop on Advanced Image Technology 2010 (2010)
Frahling, G., Sohler, C.: A Fast k-Means Implementation using Coresets. In: Proceedings of the Twenty-Second Annual Symposium on Computational Geometry, SoCG (2006)
Mount, M.D., Kanungo, T., Nethanyahu, N.S.: An Efficient k-Means Clustering Algorithm Analysis and Implementation. IEEE Transaction of Pattern Analysis and Machine Intelligence 24, 881–891 (2002)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural Features for Image Classification. IEEE Transactions on Systems, Man and Cybernatics, 610–621 (1973)
Tou, J.Y., Khoo, K.K.Y., Tay, Y.H., Lau, P.Y.: Evaluation of Speed and Accuracy for Comparison of Texture Classification Implementation. In: Proceedings International Workshop on Advanced Image Processing 2009 (2009)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences, and Trends of the New Age. ACM Computing Surveys 40(2) (2008)
Ojala, T., Pietikainen, M., Kyllonen, J.: Gray level Cooccurrence Histograms via Learning Vector Quantization. In: Proc. 11th Scandinavian Conference on Image Analysis, pp. 103–108 (1999)
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Tou, J.Y., Yong, C.Y. (2013). k-Means Clustering on Pre-calculated Distance-Based Nearest Neighbor Search for Image Search. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36543-0_2
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DOI: https://doi.org/10.1007/978-3-642-36543-0_2
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