A Fast Image Retrieval System Based on Color-Space and Color-Texture Features

We propose a practical image retrieval scheme to retrieve images efficiently. We succeed in transferring the image retrieval problem to sequences comparison and subsequently using the color sequences comparison along with the texture feature of Gray Level Co-occurrence matrix to compare the images of database. Thus the computational complexity is decreased obviously. Our results illustrate it has virtues of both the content based image retrieval system and a text based image retrieval system. Experimental results reveal that proposed scheme is better than the conventional methodologies.

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An improved image retrieval system using optimized FCM & multiple shape, texture features

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) ◽

10.1109/iccic.2015.7435666 ◽

2015 ◽

Cited By ~ 1

Author(s):

N. Neelima ◽

E. Sreenivasa Reddy

Keyword(s):

Image Retrieval ◽

Retrieval System ◽

Texture Features ◽

Image Retrieval System

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CONTENT BASED IMAGE RETRIEVAL SYSTEM BY FUSION OF COLOR, TEXTURE AND EDGE FEATURES WITH SVM CLASSIFIER AND RELEVANCE FEEDBACK

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v6.i9.2018.1230 ◽

2018 ◽

Vol 6 (9) ◽

pp. 259-273

Author(s):

Priyanka Saxena ◽

Shefali

Keyword(s):

Image Retrieval ◽

Relevance Feedback ◽

Retrieval System ◽

Semantic Gap ◽

Content Based Image Retrieval ◽

Visual Features ◽

Image Retrieval System ◽

Combined Features ◽

Edge Features ◽

Color Texture

Content Based Image Retrieval system automatically retrieves the most relevant images to the query image by extracting the visual features instead of keywords from images. Over the years, several researches have been conducted in this field but the system still faces the challenge of semantic gap and subjectivity of human perception. This paper proposes the extraction of low-level visual features by employing color moment, Local Binary Pattern and Canny Edge Detection techniques for extracting color, texture and edge features respectively. The combination of these features is used in conjunction with Support Vector Machine to reduce the retrieval time and improve the overall precision. Also, the challenge of semantic gap between low and high level features is addressed by incorporating Relevance Feedback. Average precision value of 0.782 was obtained by combining the color, texture and edge features, 0.896 was obtained by using combined features with SVM, 0.882 was obtained by using combined features with Relevance Feedback to overcome the challenge of semantic gap. Experimental results exhibit improved performance than other state of the art techniques.

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Color Texture based Image Retrieval System

International Journal of Computer Applications ◽

10.5120/2958-3910 ◽

2011 ◽

Vol 24 (5) ◽

pp. 24-29 ◽

Cited By ~ 6

Author(s):

Rahul Mehta ◽

Nishchol Mishra ◽

Sanjeev Sharma

Keyword(s):

Image Retrieval ◽

Retrieval System ◽

Image Retrieval System ◽

Color Texture

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Image Recognition Based on Shape and Texture Features

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.127 ◽

2014 ◽

Vol 536-537 ◽

pp. 127-130

Author(s):

Kun Geng

Keyword(s):

Feature Extraction ◽

Image Retrieval ◽

Retrieval System ◽

Texture Features ◽

Morphological Processing ◽

Content Based Image Retrieval ◽

Shape Features ◽

Key Technologies ◽

Image Retrieval System ◽

Important Position

Based on the shape of the image retrieval occupy an important position in the content-based image retrieval, and studied architecture, content-based image retrieval system, ie research-based image retrieval key technologies shape features for image noise in addition to the morphological processing; image segmentation; shape-based feature extraction and regional boundaries and description techniques and similarity measure techniques. The results show that the algorithm can effectively identify the characteristics of the image.

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