TY  - JOUR
T1  - Optimized Image Retrieval Using HSV Color Space, Local Edge
Binary Patterns and Zernike Moments
AU - Sucharitha, G. AU - K. Senapati, Ranjan 
JO  - Journal of Engineering and Applied Sciences
VL  - 13
IS  - 16
SP  - 6777
EP  - 6786
PY  - 2018
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2018.6777.6786
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2018.6777.6786
KW  - Local features
KW  -local edge binary pattern
KW  -Zernike moments
KW  -HSV color space
KW  -Corel-10k database
KW  -Mit-Vistex database
KW  -histogram
AB  - This study presents a new approach for image retrieval in extracting and integrating the color, texture
and shape features. The proposed descriptor converts the RGB color image into HSV color space. HSV color
space is used in this approach make use of color, intensity and brightness of the color image. From the Hue (H)
and Saturation (S) color features are extracted and from value color space texture features are extracted. To
extract the texture features from the value component Local Maximum Edge Binary Patterns are applied
(LMEBP). Apply Zernike moments on gray scale image to extract the shape features. To extract the feature
vector all the histograms are concatenated three experiments have been carried out in demonstrating the worth
of our approach. The presented method is tested on two databases, Corel-10k and MIT-Vistex. The retrieval
performance has shown a significant improvement in terms of precision and recall as compared with
Center-Symmetric Local Binary Pattern (CS-LBP) Local Edge Pattern for Segmentation (LEPSEG) and Local Edge
Pattern for Image Retrieval (LEPINV) and other existing transform techniques in image retrieval system.
ER  -