Effectiveness of Image (dis)similarity Algorithms on Content- Based Image Retrieval

Dis) similarity measure is a significant component of vector model. In content based image retrieval the compatibility of (dis)similarity measure and representation technique is very important for effective and efficient image retrieval. In order to find a suitable dis-similarity measure for a particular representation technique experimental comparison is needed. This paper highlights some of the (dis)similarity algorithms available in literature then compares Euclidean dissimilarity and cosine similarity on Kernel Density Feature Points Estimator (KDFPE) image representation technique. The retrieval results show that cosine similarity had slightly better retrieval rate than Euclidean dissimilarity.

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