Local Image Features for Shoeprint Image Retrieval

Abstract

This paper deals with the retrieval of scene-of-cri me (or scene) shoeprint images from a reference database of shoeprint images by using a n ew local feature detector and an improved local feature descriptor. Our approach is based on novel modifications and improvements of a few recent techniques in this area: (1) the scale a dapted Harris detector, which is an extension to multi-scale domains of the Harris corner detector; (2) automatic scale selection by the characteristic scale of a local structure. (3) SIFT (Scale-Invariant Feature Transform), one of the most widely investigated descriptors in recent year s. Like most of other local feature representations, the proposed approach can also be divided into two stages: (i) a set of distinctive local features are selected by first de tecting scale adaptive Harris corners where each of them is associated with a scale factor, and then selecting as the final result only those corners whose scale matches the scale of blob-like structur es around them. Here, the scale of a blob-like structure is detected by the Laplace-based scale se lection. (ii). for each feature detected, an enhanced SIFT descriptor is computed to represent t his feature. Our improvements lead two novel methods which we call the Modified Harris-Laplace (MHL) detector, and the enhanced SIFT descriptor. In this paper, we demonstrate the application of t he proposed scheme to the shoeprint image retrieval problem using six sets of synthetic scene images, 50 images for each, and a database of 500 reference shoeprint images. The ret rieval performance of the proposed approach is significantly better, in terms of cumul ative matching score, than the existing methods used in this application area, such as edge directional histogram, power spectral distribution, and pattern & topological spectra.