Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Digital Object Identifier (DOI) : 10.14569/IJACSA.2015.060929
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 9, 2015.
Abstract: Image retrieval is still an active research topic in the computer vision field. There are existing several techniques to retrieve visual data from large databases. Bag-of-Visual Word (BoVW) is a visual feature descriptor that can be used successfully in Content-based Image Retrieval (CBIR) applications. In this paper, we present an image retrieval system that uses local feature descriptors and BoVW model to retrieve efficiently and accurately similar images from standard databases. The proposed system uses SIFT and SURF techniques as local descriptors to produce image signatures that are invariant to rotation and scale. As well as, it uses K-Means as a clustering algorithm to build visual vocabulary for the features descriptors that obtained of local descriptors techniques. To efficiently retrieve much more images relevant to the query, SVM algorithm is used. The performance of the proposed system is evaluated by calculating both precision and recall. The experimental results reveal that this system performs well on two different standard datasets.
Mohammed Alkhawlani, Mohammed Elmogy and Hazem Elbakry, “Content-Based Image Retrieval using Local Features Descriptors and Bag-of-Visual Words” International Journal of Advanced Computer Science and Applications(IJACSA), 6(9), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060929