Content-Based Image Retrieval By Relevance Feedback

Abstract

Abstract. Relevance feedback i s a p o werful technique for content-based image retrieval. Many parameter estimation approaches have been proposed for relevance feedback. However, most of them have only utilized information of the relevant retrieved images, and have given up, or have not made great use of information of the irrelevant retrieved images. This paper presents a novel approach to update the interweights of integrated probability function by using the information of both relevant and irrelevant retrieved images. Experimental results have s h o wn the e ectiveness and robustness of our proposed approach, especially in the situation of no relevant retrieved images.

Interactive Image Retrieval with Wavelet Features

“…EMD distance is computed between the query image and the stored image signature in the database and the images are displayed in the first pass. (6). Feature Evaluation Index (FEI) are calculated from the marked relevant and irrelevant set of images and recomputes the weighted EMD over each iteration.…”

“…Zin et al [6] have proposed a feature re-weighting technique by using both the relevant and the irrelevant information, to obtain more effective results.…”

Novel CBIR system based on Ripplet Transform using interactive Neuro-Fuzzy technique

“…Most of the RFMs, employ two approaches namely, query vector moving technique and feature re-weighting technique to improve the retrieval results [9]. Feature re-weighting technique uses both the relevant and the irrelevant information, to obtain more effective results [10,11]. But in all these cases, time complexity per iteration are high and accuracy of the relevant images are low.…”

Interactive Content Based Image Retrieval Using Ripplet Transform and Fuzzy Relevance Feedback

“…Most of the RFM, employ two approaches namely, query vector moving technique and feature re-weighting technique to improve the retrieval results [7]. Feature re-weighting technique utilizes both the relevant and the irrelevant result's information, to obtain more effective result [2,8]. But in all these cases time complexity per iteration are high and the accuracy of the relevant images are low.…”