Content Based Image Retrieval Based on Log Gabor Wavelet Transform

2011 ◽  
Vol 403-408 ◽  
pp. 871-878 ◽  
Author(s):  
Megha Agarwal ◽  
Rudra Prakash Maheshwari
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Retrieval System ◽  
Gabor Wavelet ◽  
Content Based Image Retrieval ◽  
Natural Image ◽  
Feature Descriptor ◽  
Gabor Wavelet Transform ◽  
Log Gabor Wavelet ◽  
Log Gabor

This paper proposes a novel approach of content based image retrieval based on Log Gabor Wavelet Transform (LGWT). It is observed that LGWT better represents an image compared to Gabor Wavelet Transform (GWT). Experimental results illustrate the comparative analysis of proposed retrieval system and the retrieval system based on GWT feature descriptor. It is verified that LGWT based retrieval system improves the average precision and average recall (55.46% and 32.03% respectively) from GWT based retrieval system (50.61% and 31.63% respectively). All the experiments are performed on Corel 1000 natural image database.

scholarly journals Binary Wavelet Transform Based Histogram Feature for Content Based Image Retrieval

2011 ◽  
pp. 68-75 ◽  
Author(s):  
Megha Agarwal ◽  
R. P. Maheshwari
Keyword(s):  
Wavelet Transform ◽  
Image Retrieval ◽  
Discrete Wavelet Transform ◽  
Content Based Image Retrieval ◽  
Natural Image ◽  
Discrete Wavelet ◽  
Average Precision ◽  
Average Recall ◽  
Retrieval Rate ◽  
Histogram Feature

In this paper a new visual feature, binary wavelet transform based histogram (BWTH) is proposed for content based image retrieval. BWTH is facilitated with the color as well as texture properties. BWTH exhibits the advantages of binary wavelet transform and histogram. The performance of CBIR system with proposed feature is observed on Corel 1000 (DB1) and Corel 2450 (DB2) natural image database in color as well as gray space. The results analysis of DB1 database illustrates the better average precision and average recall of proposed method in RGB space (73.82%, 44.29%) compared to color histogram (70.85%, 42.16%), auto correlogram (66.15%, 39.52%) and discrete wavelet transform (60.83%, 38.25%). In case of gray space also performance of proposed method (66.69%, 40.77%) is better compared to auto correlogram (57.20%, 35.31%), discrete wavelet transform (52.70%, 32.98%) and wavelet correlogram (64.3%, 38.0%). It is verified that in case of DB2 database also average precision, average recall and average retrieval rate of proposed method are significantly better.

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