Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval

TL;DR

This paper introduces a novel content-based image retrieval framework that automatically determines feature weights using indexing and pseudo-relevance feedback, improving retrieval accuracy across multiple datasets.

Contribution

It proposes an automatic feature weighting method using relevance ratio and mean difference, integrated with multiclass SVM indexing for enhanced CBIR performance.

Findings

Outperforms existing techniques on benchmark datasets

Automatically determines feature weights for better retrieval accuracy

Validates effectiveness through extensive experiments

Abstract

Content-based image retrieval (CBIR) is one of the most active research areas in multimedia information retrieval. Given a query image, the task is to search relevant images in a repository. Low level features like color, texture, and shape feature vectors of an image are always considered to be an important attribute in CBIR system. Thus the performance of the CBIR system can be enhanced by combining these feature vectors. In this paper, we propose a novel CBIR framework by applying to index using multiclass SVM and finding the appropriate weights of the individual features automatically using the relevance ratio and mean difference. We have taken four feature descriptors to represent color, texture and shape features. During retrieval, feature vectors of query image are combined, weighted and compared with feature vectors of images in the database to rank order the results. Experiments were performed on four benchmark datasets and performance is compared with existing techniques to validate the superiority of our proposed framework.