Distance-from-boundary as a metric for texture image retrieval

Abstract

A new metric is proposed for texture image retrieval, which is based on the signed distance of the images in the database to a boundary chosen by the query. This novel metric has three advantages: (1) the boundary distance measures are relatively insensitive to the sample distributions; (2) the same retrieval results can be obtained with respect to different (but visually similar) queries; (3) retrieval performance can be improved. The boundaries are obtained by using a statistical learning algorithm called support vector machine (SVM), and hence the boundaries can be simply represented by some vectors and their combination coefficients. Experimental results on the Brodatz texture database indicate that a significantly better retrieval performance can be achieved as compared to the traditional Euclidean distance-based approach. This technique can be further developed to learn pattern similarities among different texture classes and used in relevance feedback.