Design and implementation of connoisseur annotated metadata based image retrieval system -a case study

2010 ◽  
Author(s):  
Vinod Kumar ◽  
Sushma Lehri ◽  
Ashok Kumar Sharma ◽  
Arvind Kumar
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Design And Implementation ◽  
System A ◽  
Image Retrieval System

scholarly journals Unsupervised Content Based Image Retrieval by Combining Visual Features of an Image With A Threshold

2012 ◽  
pp. 149-154
Author(s):  
S. M. Zakariya ◽  
Rashid Ali ◽  
Nesar Ahmad
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
Image Database ◽  
The Other ◽  
Content Based Image Retrieval ◽  
Visual Features ◽  
Query Image ◽  
System A ◽  
Image Retrieval System

Content-based image retrieval (CBIR) uses the visual features of an image such as color, shape and texture to represent and index the image. In a typical content based image retrieval system, a set of images that exhibit visual features similar to that of the query image are returned in response to a query. CLUE (CLUster based image rEtrieval) is a popular CBIR technique that retrieves images by clustering. In this paper, we propose a CBIR system that also retrieves images by clustering just like CLUE. But, the proposed system combines all the features (shape, color, and texture) with a threshold for the purpose. The combination of all the features provides a robust feature set for image retrieval. We evaluated the performance of the proposed system using images of varying size and resolution from image database and compared its performance with that of the other two existing CBIR systems namely UFM and CLUE. We have used four different resolutions of image. Experimentally, we find that the proposed system outperforms the other two existing systems in ecery resolution of image.

Contourlet-1.3 Texture Retrieval Using Absolute Mean Energy and Kurtosis Features

2011 ◽  
Vol 48-49 ◽  
pp. 327-330
Author(s):  
Xin Wu Chen ◽  
Li Wei Liu
Keyword(s):  
Image Retrieval ◽  
Retrieval System ◽  
System Structure ◽  
Texture Image ◽  
Feature Vectors ◽  
System A ◽  
Retrieval Rate ◽  
Image Retrieval System ◽  
Canberra Distance ◽  
Texture Retrieval

To improve the texture image retrieval rate of contourlet texture image retrieval system, a contourlet-1.3 transform based texture image retrieval system was proposed. In the system, the contourlet transform was contourlet-1.3, a new version of the original contourlet, sub-bands absolute mean energy and kurtosis in each contourlet-1.3 sub-band were cascaded to form feature vectors, and the similarity metric was Canberra distance. Experimental results on 109 brodatz texture images show that using the features cascaded by absolute mean energy and kurtosis can lead to a higher retrieval rate than the combination of standard deviation and absolute mean energy which is most commonly used today under same dimension of feature vectors. Contourlet-1.3 transform based image retrieval system is superior to those of the original contourlet, non-subsampled contourlet and contourlet-2.3 systems under same system structure with same dimension of feature vectors, retrieval time and memory needed.

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