Comparative Study and Optimization of Feature-Extraction Techniques for Content based Image Retrieval

TL;DR

This paper compares various feature-extraction techniques for content-based image retrieval, evaluates their combinations, and introduces query modification through image cropping to enhance retrieval accuracy.

Contribution

It provides a comprehensive comparison of feature-extraction methods and proposes a novel query modification approach to improve CBIR performance.

Findings

Optimal feature combinations vary for different image classes

Query cropping significantly improves retrieval accuracy

Proposed method enhances personalization of image search results

Abstract

The aim of a Content-Based Image Retrieval (CBIR) system, also known as Query by Image Content (QBIC), is to help users to retrieve relevant images based on their contents. CBIR technologies provide a method to find images in large databases by using unique descriptors from a trained image. The image descriptors include texture, color, intensity and shape of the object inside an image. Several feature-extraction techniques viz., Average RGB, Color Moments, Co-occurrence, Local Color Histogram, Global Color Histogram and Geometric Moment have been critically compared in this paper. However, individually these techniques result in poor performance. So, combinations of these techniques have also been evaluated and results for the most efficient combination of techniques have been presented and optimized for each class of image query. We also propose an improvement in image retrieval performance by introducing the idea of Query modification through image cropping. It enables the user to identify a region of interest and modify the initial query to refine and personalize the image retrieval results.