Improving Content Based Image Retrieval using Scale Invariant Feature Transform
Mamta Kamath1, Disha Punjabi2, Tejal Sabnis3, Divya Upadhyay4, Seema Shrawne5
1Mamta Kamath, Computer Technology Department, Veermata Jijabai Technological Institute, Mumbai, Maharashtra, India.
2Disha Punjabi, Computer Technology Department, Veermata Jijabai Technological Institute, Mumbai, Maharashtra, India.
3Tejal Sabnis, Computer Technology Department, Veermata Jijabai Technological Institute, Mumbai, Maharashtra, India.
4Divya Upadhyay, Computer Technology Department, Veermata Jijabai Technological Institute, Mumbai, Maharashtra, India.
5Seema Shrawne, Computer Technology Department, Veermata Jijabai Technological Institute, Mumbai, Maharashtra, India.
Manuscript received on may 27, 2012. | Revised Manuscript received on June 22, 2012. | Manuscript published on June 30, 2012. | PP: 19-21 | Volume-1 Issue-5, June 2012 | Retrieval Number: E0377041512/2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Content-Based Image Retrieval (CBIR) is a challenging task. Common approaches use only low-level features. Notwithstanding, such CBIR solutions fail on capturing some local features representing the details and nuances of scenes. Many techniques in image processing and computer vision can capture these scene semantics. Among them, the Scale Invariant Features Transform (SIFT) has been widely used in a lot of applications. This approach relies on the choice of several parameters which directly impact its effectiveness when applied to retrieve images. In this paper, we attempt to evaluate the application of the SIFT to refine CBIR.
Keywords: Content Based Image Retrieval (CBIR), Difference of Gaussian (DOG), Nearest Neighbour Search (NNS), Scale Invariant Feature Transform (SIFT).