doi: 10.7763/IJET.2011.V3.283
Content Based Image Retrieval Using Combination of Kekre Transform and HSV Color Segmentation
Venu Shah
,
Jackie Bhavsar
- College of Engineering, Malad (W), Mumbai-400095, India
Abstract
This paper proposes a content-based image retrieval system based on an efficient combination of both feature and color algorithms. According to Kekre Transform we derive feature vectors using a combination of row mean and column mean of both query as well as the database images, to measure the extent of similarity in features using Euclidian distance. Experimentation shows that taking row mean, column mean and combination improves the performance of image retrieval as compared to taking Kekre Transform of the whole image. Similarly, HSV colour space quantifies the colour space into different regions and thereby calculating its mean and Euclidian distance the colour vector can be derived. Taking mean or Euclidian distance of the individual Euclidian distances of both the algorithms improves the accuracy of the image retrieval process implemented. By calculating the precision and recall parameters of selective images from the database, comparison between the two algorithms and the effectiveness of the combination of both the algorithms can be measured.
Keywords
- CBIR
- Feature Vector
- Color Vector
- Euclidian distance
- HSV
- Hue
- Adaptive Segmentation
How to Cite
Venu Shah and Jackie Bhavsar, "Content Based Image Retrieval Using Combination of Kekre Transform and HSV Color Segmentation," International Journal of Engineering and Technology, vol. 3, no. 5, pp. 547-552, 2011. https://doi.org/ 10.7763/IJET.2011.V3.283
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).