A Survey on Content-Based Retrieval for Multimedia Databases
IEEE Transactions on Knowledge and Data Engineering
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast k-means implementation using coresets
Proceedings of the twenty-second annual symposium on Computational geometry
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
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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%.