Relevance feature mapping for content-based image retrieval

Abstract

This paper presents a ranking framework for content-based image retrieval using relevance feature mapping. Each relevance feature measures the relevance of an image to some profile underlying the image database. The framework is a two-stage process. In the off-line modeling stage, it constructs a collection of models which maps all images in the database to the relevance feature space. In the on-line retrieval stage, it assigns a weight to every relevance feature based on the query image, and then ranks images in the database according to their weighted average feature values. The framework also incorporates relevance feedback which modifies the ranking based on the feedbacks through reweighted features. We show that the power of the proposed framework is coming from the relevance features. Experiments on a large image database validate the efficacy and efficiency of the proposed framework.

Mass estimation and its applications

“…We have already established a direct application of mass in content-based image retrieval [17]. In addition to the mass-space mapping we have shown here, [17] presents a framework that assigns a weight (based on iForest, thus, mass) to each feature w.r.t.…”

“…In addition to the mass-space mapping we have shown here, [17] presents a framework that assigns a weight (based on iForest, thus, mass) to each feature w.r.t. a query image; and then it ranks images in the database according to their weighted average feature values.…”