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Image Retrieval using Row Means of Column Transformed Even and Odd Parts of Image with Walsh, Haar and Kekre Transforms

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Author(s): H.B. Kekre | Sudeep D. Thepade | Akshay Maloo

Journal: International Journal of Computer Science and Information Security
ISSN 1947-5500

Volume: 8;
Issue: 5;
Start page: 79;
Date: 2010;
Original page

Keywords: IJCSIS | call for paper | journal computer science | research | google scholar | IEEE | Scirus | download | ArXiV | library | information security | internet | peer review | scribd | docstoc | cornell university | archive | Journal of Computing | DOAJ | Open Access | August 2010 | Volume 8 | No. 5 | Impact Factor

ABSTRACT
The paper presents an innovative content based image retrieval (CBIR) technique based on row mean of column transformed even and odd part of image. Here the performance of proposed CBIR technique is tested for different transforms like Walsh transform, Haar transform and Kekre transform. Instead of using all pixels of image as feature vector for image retrieval, row mean of transform applied on columns of odd and even part of image is used, resulting into better performance with much lower computations. The proposed CBIR techniques are tested on generic image database having 1000 images spread across 11. For each transform 55 queries (5 per category) were fired. Then the precision and recall for all queries are computed. While considering the relevance of result images for calculation of precision and recall the results of odd image part and even image part both are ORED. The average precision and average recall values of all queries gives the performance comparison of proposed CBIR methods as compared to considering full image data as feature vector or considering the simple row mean as feature vector. The results have shown the performance improvement (higher precision and recall values) with proposed methods compared to all pixel data of image at reduced computations resulting in faster retrieval. The comparison of transforms for CBIR performance gives Walsh transform surpassing the other two considered here.
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