Abstract

Feature integration theory can be regarded as a perception theory, but the extraction of visual features using such a theory within the CBIR framework is a challenging problem. To address this problem, we extract the color and edge features based on a multi-integration features model and use these for image retrieval. A novel and highly simple but efficient visual feature descriptor, namely, a multi-integration features histogram, is proposed for image representation and content-based image retrieval. First, a color image is converted from the RGB to the HSV color space, and the color features and color differences are extracted. Then, the color differences are calculated to extract the edge features using a set of simple integration processes. Finally, combining the color, edge, and spatial layout features allows representing the image content. Experiments show that our method produces results comparable to existing and well-known methods on three datasets that contain 25,000 natural images. The performances are significantly better than that of the BOW histogram, local binary pattern histogram, histogram of oriented gradient, and multi-texton histogram, with performances similar to the color volume histogram.

Highlights

  • Introduction eCBIR technique originated in the 1990s and has become a research hotspot over the last thirty years. e CBIR has been used to describe the process of retrieving similar images from a large collection based on the image content. e essence of the traditional CBIR is associated with similar image retrieval

  • Many descriptors can be used to characterize the texture characteristics, where the local binary pattern (LBP) is a wellknown texture descriptor that can represent the local structures of image, but it cannot combine color and edge cues well. e multi-texton histogram (MTH) method is our prior work and was developed for the traditional CBIR, and it can represent the frequency for color and edge orientation information using a special histogram

  • CPV-THF, STH, and CMSD methods were derived from our previous works, but those extend methods have not extracted the color and edge features based on feature integration theory

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Summary

Introduction

Introduction eCBIR technique originated in the 1990s and has become a research hotspot over the last thirty years. e CBIR has been used to describe the process of retrieving similar images from a large collection based on the image content (color, shape, and texture). e essence of the traditional CBIR is associated with similar image retrieval. E CBIR has been used to describe the process of retrieving similar images from a large collection based on the image content (color, shape, and texture). E second stage is focused attention, in which we take all the observed edge features and combine them to make a complete perception [1]. Representing image contents via extracting the color and edge features based on feature integration theory is a challenging problem. To address this problem, a novel and highly simple but efficient representation based on the multi-integration features model, namely the multi-integration features histogram (MIFH), is proposed for CBIR

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