Combining content and context similarities for image retrieval

Abstract

CBIR has been a challenging problem and its performance relies on the underlying image similarity (distance) metric. Most existing metrics evaluate pairwise image similarity based only on image content, which is denoted as content similarity. In this study we propose a novel similarity metric to make use of the image contexts in an image collection. The context of an image is built by constructing a vector with each dimension representing the content similarity between the image and any image in the image collection. The context similarity between two images is obtained by computing the similarity between the corresponding context vectors using the vector similarity functions. The content similarity and the context similarity are then combined to evaluate the overall image similarity. Experimental results demonstrate that the use of the context similarity can significantly improve the retrieval performance. © Springer-Verlag Berlin Heidelberg 2009.