Emphasis on rough Set theory for image retrieval

Khodaskar, Anuja; Ladhake, S. A.

doi:10.1109/iccci.2015.7218059

Cited by 1 publication

(1 citation statement)

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“…With the rapid development of modern computer technology and soft computing theory, many modern algorithms and theories are applied to the image segmentation and image retrieval, which include fuzzy sets [12], rough set theory [13][14][15][16][17][18][19][20], near sets [21], mathematical morphology, neural network, etc. Computational Proximity (CP) [21] is a novel method to deal with image processing, which can identify nonempty sets of points that are either close to each other or far apart by comparing descriptions of pixels points or region in digital images or videos.…”

Section: Introductionmentioning

confidence: 99%

Color-Based Image Retrieval Using Proximity Space Theory

Wang

Liu

et al. 2018

Algorithms

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The goal of object retrieval is to rank a set of images by their similarity compared with a query image. Nowadays, content-based image retrieval is a hot research topic, and color features play an important role in this procedure. However, it is important to establish a measure of image similarity in advance. The innovation point of this paper lies in the following. Firstly, the idea of the proximity space theory is utilized to retrieve the relevant images between the query image and images of database, and we use the color histogram of an image to obtain the Top-ranked colors, which can be regard as the object set. Secondly, the similarity is calculated based on an improved dominance granule structure similarity method. Thus, we propose a color-based image retrieval method by using proximity space theory. To detect the feasibility of this method, we conducted an experiment on COIL-20 image database and Corel-1000 database. Experimental results demonstrate the effectiveness of the proposed framework and its applications.

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