Fast content-based image retrieval based on equal-average K-nearest-neighbor search schemes

Abstract

The four most important issues in content-based image retrieval (CBIR) are how to extract features from an image, how to represent these features, how to search the images similar to the query image based on these features as fast as we can and how to perform relevance feedback. This paper mainly concerns the third problem. The traditional features such as color, shape and texture are extracted offline from all images in the database to compose a feature database, each element being a feature vector. The "linear scaling to unit variance" normalization method is used to equalize each dimension of the feature vector. A fast search method named equal-average K nearest neighbor search (EKNNS) is then used to find the first K nearest neighbors of the query feature vector as soon as possible based on the squared Euclidean distortion measure. Experimental results show that the proposed retrieval method can largely speed up the retrieval process, especially for large database and high feature vector dimension. © Springer-Verlag Berlin Heidelberg 2006.