Image Search Engine Using SIFT Algorithm
DOI:
https://doi.org/10.53555/nncse.v2i3.486Keywords:
Histogram, RGB values, Comparison of two images based on Histogram, Image Database, Threshold, Experimental ResultsAbstract
The approach of SIFT feature detection taken in our implementation is similar with the one taken by Lowe, which is used for object recognition. According to Lowe’s work, the invariant features extracted from images can be used to perform reliable matching between different views of an object or scene. The features can be different from image rotation and scale and robust across a substantial range of various distortion, addition of various other colors ,and change in actual view of the image .The approach is efficient on feature extraction and has the ability to identify large numbers of features .In short changes image has will not be mind by our process in order to match the image where basically images are going to be matched using Histogram and RGB values of the image present in the Database of Admin i.e Search Engine itself and the image asked by the User to searched.
References
Zhuozeng Wang; Yalei Mei; Fang Yan (2009, 8 November), ‘Web Image Search engine Using SIFT Algorithm’. IEEE International conference on Web Information Systems and Mining, 2009. WISM 2009. Pp. 366-370, 2009.
Nabeel Younus Khan, Brendan McCane, and Geoff Wyvill, “SIFT and SURF Performance Evaluation against Various Image Deformations on Benchmark Dataset”, International Conference on Digital Image Computing: Techniques and Applications, pp.501-506, 2011.
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