Efficient Region-Based Image Querying

TL;DR

This paper introduces a fast, multi-level neural network approach for region-based image classification and retrieval, effectively isolating significant image regions and classifying them into predefined categories to improve retrieval accuracy.

Contribution

It presents a novel multi-level neural network model combined with mean-shift segmentation and feature extraction for efficient region-based image retrieval.

Findings

High classification accuracy compared to state-of-the-art methods

Effective segmentation and feature extraction process

Promising retrieval performance in large image repositories

Abstract

Retrieving images from large and varied repositories using visual contents has been one of major research items, but a challenging task in the image management community. In this paper we present an efficient approach for region-based image classification and retrieval using a fast multi-level neural network model. The advantages of this neural model in image classification and retrieval domain will be highlighted. The proposed approach accomplishes its goal in three main steps. First, with the help of a mean-shift based segmentation algorithm, significant regions of the image are isolated. Secondly, color and texture features of each region are extracted by using color moments and 2D wavelets decomposition technique. Thirdly the multi-level neural classifier is trained in order to classify each region in a given image into one of five predefined categories, i.e., "Sky", "Building", "SandnRock", "Grass" and "Water". Simulation results show that the proposed method is promising in terms of classification and retrieval accuracy results. These results compare favorably with the best published results obtained by other state-of-the-art image retrieval techniques.