Mid-level feature based local descriptor selection for image search

Abstract

The objective in developing compact descriptors for visual image search is building an image retrieval system that works efficiently and effectively under bandwidth and memory constraints. Selecting local descriptors to be processed, and sending them to the server for matching is an integral part of such a system. One such image search and retrieval system is the Compact Descriptors for Visual Search (CDVS) standardization test model being developed by MPEG which has an efficient local descriptor selection criteria. However, all the existing selection parameters in CDVS are based on low-level features. In this paper, we propose two “mid-level” local descriptor selection criteria: Visual Meaning Score (VMS), and Visual Vocabulary Score (VVS) which can be seamlessly integrated into the existing CDVS framework. A mid-level criteria explicitly allows selection of local descriptors closer to a given set of images. Both VMS and VVS are based on visual words (patches) of images, and provide significant gains over the current CDVS standard in terms of matching accuracy, and have very low implementation cost.