Multiple Cartesian K-Medoids for a Fine Quantization

K-means is a widely used method for the process of vector quantization in image retrieval, and its results will directly affect the subsequent retrieval quality. Although k-means is popular in image retrieval, it has some obvious disadvantages, such as randomness and sensitivity to outliers. This paper presents a new model, namely Multiple Cartesian K-medoids, to replace k-means for quantization and retrieval. The proposed model proceeds in two steps. The first step is to establish multiple K-medoids model to finely quantize feature vectors to codewords. Then, the second step establishes local linear search: adopt an inverted file for efficiently searching candidate nearest neighbors of a given query, and finally obtains accurate neighbors of the query by re-ranking these candidate neighbors with Euclidean distances of the original feature vectors. Experimental results show that the proposed method is effective, and substantially improves the search accuracy of the returned nearest neighbors.