Learning “initial feature weights” for CBIR using query augmentation

Abstract

Content-based image retrieval (CBIR) is one of the most active fields of research in image processing and information retrieval. In CBIR, an image is given as query, instead of text, and a set of relevant images is returned as an output. Researchers have generally used multiple image features in conjunction to achieve high CBIR performance. Relevance feedback has also seen widespread adoption as technique to utilise user feedback to further refine search results. In this paper, we propose a technique to ascertain the initial feature weights before the actual query is processed. The weights of the features are determined by augmenting the query image through different transformations of the query image. The proposed method is tested on the VisTex and Outex_TR_00000 texture collections. The performance is measured by average retrieval rate, precision and recall. Our results do not show any degradation on retrieval performance on collections that have relevance classes which are generally uniform. However, on collections that are more heterogeneous, our proposed method leads to better search results.