Author(s): A. Gayathri, A. Srinivasan
Published in: International Journal of Engineering Research & Technology
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Volume/Issue: Volume. 6 - Issue. 08 , August - 2017
Content-based image retrieval aims to retrieve images from a database which are similar to each other in terms of their visual contents. Over the years, various feature descriptors based on color, texture and shape information have been proposed to characterize visual elements of image and video data. These features have subsequently been employed to improve the automated search and retrieval of image and videos in CBIR applications. The applications of CBIR are: users searching a particular image on the web, Various types of professionals like police force for picture recognition in crime prevention, Medicine diagnosis, Architectural and engineering design, Fashion and publishing, Geographical information, remote sensing systems and home entertainment. However, selecting and developing methods for correctly identifying and effectively integrating suitable visual features for a specific vision task remain a very challenging problem. The proposed method uses support vector machines for retrieval of images based on content. First, 190 dimensional feature vectors are extracted and similarity metrics is defined. Using the SVM learning techniques, the images which are similar to the query image are retrieved.
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