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Integration of Color and Texture Features in CBIR System

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Hany F. Atlam, Gamal Attiya, Nawal El-Fishawy
10.5120/ijca2017913600

Hany F Atlam, Gamal Attiya and Nawal El-Fishawy. Integration of Color and Texture Features in CBIR System. International Journal of Computer Applications 164(3):23-29, April 2017. BibTeX

@article{10.5120/ijca2017913600,
	author = {Hany F. Atlam and Gamal Attiya and Nawal El-Fishawy},
	title = {Integration of Color and Texture Features in CBIR System},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2017},
	volume = {164},
	number = {3},
	month = {Apr},
	year = {2017},
	issn = {0975-8887},
	pages = {23-29},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume164/number3/27464-2017913600},
	doi = {10.5120/ijca2017913600},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Nowadays, rapid and effective searching for relevant images in large image databases has become an area of wide interest in many applications. The current image retrieval system is based on text-based approaches. This system has many challenges such as it cannot retrieve images that are context sensitive and the amount of effort required to manually annotate every image, as well as the difference in human perception when describing the images, which result in inaccuracies during the retrieval process. Content-based image retrieval (CBIR) supports an effective way to retrieve images depending on automatically derived image features. It retrieves relevant images using unique image features such as texture, color or shape.

This paper presents novel methods to retrieve relevant images from large image databases. Two proposed methods are presented. The first proposed method improves the retrieval performance by identifying the most efficient gray-level co-occurrence matrix (GLCM) texture features and combine them with the appropriate Discrete Wavelet Transform (DWT) decomposition band. The second proposed method increases the system performance by combining color and texture features as one feature vector which is resulting in increasing the retrieval accuracy. The proposed methods have shown a promising and faster retrieval on a WANG image database containing 1000 color images. The retrieval performance has been evaluated with the existing systems that discussed in the literature. The proposed methods give better performance than other systems.

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Keywords

CBIR, Color Histogram, GLCM, DWT, Image Retrieval, WANG image database, Euclidean distance