Image retrieval with relevance feedback: from heuristic weight adjustment to optimal learning methods

Abstract

Various relevance feedback algorithms have been proposed in recent years in the area of content-based image retrieval. This paper gives a brief review and analysis on existing techniques-from early heuristic-based feature weighting schemes to recently proposed optimal learning algorithms. In addition, the kernel-based biased discriminant analysis (KBDA) is proposed to fit the unique nature of relevance feedback as a biased classification problem. As a novel variant of traditional discriminant analysis, the proposed algorithm provides a trade-off between discriminant transform and regression. The kernel form is derived to deal with non-linearity in an elegant way. Experimental results indicate that significant improvement in retrieval performance is achieved by the new scheme.

Fast adaptive Mahalanobis distance-based search and retrieval in image databases

“…Hence, significant research activity (in content-based image retrieval) has been directed toward Mahalanobis (or weighted Euclidean) distances (see [4]). The Mahalanobis distance measure has more degrees of freedom than the Euclidean distance and by proper updation (or relevance feedback), has been found to be a much better estimator of user perceptions (see [5,6,4]). …”

Scalable and Efficient Cluster-Based Framework for Multidimensional Indexing

“…Therefore these data sets can be indexed really using techniques such as Euclidean Distance. The usage of ED is a significant research activity which is used in many applications including content -based image retrieval [4], [5]. In this paper we focused on the real world datasets which are high-dimensional in nature for indexing.…”

Long term learning for image retrieval over networks

“…The main idea is to build a description based on the images content, and to find similarities between descriptions [2]. Machine learning techniques have been successfully adapted to train a similarity function in interaction with the user (using her labeling of the results) leading to the so called "relevance feedback" [3,4]. The best improvement has been done with the introduction of active learning, which aims at proposing for labeling the image that will at most enhance the similarity function when added to the training set [5].…”