Content-Based Image Retrieval Performance Using Texture and Colour Features

Image retrieval has been one of the most interesting and vivid research areas in the field of computer vision over the last decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. There are various features which can be extracted from the image which gives different performance in retrieving the image.al systems. In this paper we have tried to compare the effect of using different features on the same data base to implement CBIR system. We have tried to analyse the retrieval performance for each feature. We have compared different features as well as the combinations of them to improve the performance. We have also compared the effect of different matching techniques on the retrieval process.

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Mean Normalized Retrieval Order (MNRO): a new content-based image retrieval performance measure

Multimedia Tools and Applications ◽

10.1007/s11042-012-1192-z ◽

2012 ◽

Vol 70 (3) ◽

pp. 1767-1798 ◽

Cited By ~ 11

Author(s):

Savvas A. Chatzichristofis ◽

Chryssanthi Iakovidou ◽

Yiannis S. Boutalis ◽

Elli Angelopoulou

Keyword(s):

Image Retrieval ◽

Performance Measure ◽

Content Based Image Retrieval ◽

Retrieval Performance

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Content-Based Image Retrieval through Region Uniformly Partition

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.500.471 ◽

2012 ◽

Vol 500 ◽

pp. 471-474 ◽

Cited By ~ 1

Author(s):

Xiao Xiao ◽

De Wen Zhuang ◽

Shou Jue Wang

Keyword(s):

Image Segmentation ◽

Image Retrieval ◽

Similarity Measure ◽

Open Problem ◽

Texture Features ◽

Gabor Wavelet ◽

Content Based Image Retrieval ◽

Retrieval Performance ◽

Color Features ◽

Better Than

It has been demonstrated that accurate image segmentation is still an open problem. For avoiding this difficulties in content-based image retrieval, an region uniform partition approaching was proposed. Based on fusing regional color features using smooth slide histogram and texture features extracted using Gabor wavelet, we provided the corresponding similarity measure. The image retrieval performance on a subset of the COREL database are better than SIMPLIcity system showed the effectiveness of the proposed method.

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Content-Based Image Retrieval Based on Electromagnetism-Like Mechanism

Mathematical Problems in Engineering ◽

10.1155/2013/782519 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Hamid A. Jalab ◽

Nor Aniza Abdullah

Keyword(s):

Image Retrieval ◽

Fitness Function ◽

Optimization Technique ◽

Content Based Image Retrieval ◽

Retrieval Performance ◽

Function Measurement ◽

Average Precision ◽

Query Image ◽

Two Phases ◽

Novel Algorithm

Recently, many researchers in the field of automatic content-based image retrieval have devoted a remarkable amount of research looking for methods to retrieve the best relevant images to the query image. This paper presents a novel algorithm for increasing the precision in content-based image retrieval based on electromagnetism optimization technique. The electromagnetism optimization is a nature-inspired technique that follows the collective attraction-repulsion mechanism by considering each image as an electrical charge. The algorithm is composed of two phases: fitness function measurement and electromagnetism optimization technique. It is implemented on a database with 8,000 images spread across 80 classes with 100 images in each class. Eight thousand queries are fired on the database, and the overall average precision is computed. Experimental results of the proposed approach have shown significant improvement in the retrieval performance in regard to precision.

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