Smart Query Expansion Scheme for CDVS Based on Illumination and Key Features

Abstract

Given a query image, retrieving images depicting the same object in a large scale database is becoming an urgent and challenging task. Recently, Compact Description for Visual Search (CDVS) is drafted by the ISO/TEC Moving Pictures Experts Group (MPEG) to support image retrieval applications, and it has been published as an international standard. Unfortunately, with regard to applications with hugely mutative illumination, perspective and noisy background, CDVS suffers from an inevitable performance loss. In this paper, firstly we introduce the query expansion to address performance loss caused by the scene complexity in CDVS. Secondly, a query expansion instance selection method based on illumination is proposed, which achieves better performance. Thirdly, we adopt a key feature matching score based weighted strategy in basic query expansion to improve retrieval performance. We evaluate our proposed methods on the Oxford (5K images) dataset and a reality traffic vehicle dataset (12K images), and the result shows that the proposed methods boost mean average precision (MAP) by 7% similar to 10% in Oxford dataset and 7% similar to 17% in vehicle dataset.