Efficient Image Retrieval using Dense-SIFT for Enhanced Object Segmentation and Key Point Localization
DOI:
https://doi.org/10.22399/ijcesen.2507Keywords:
CBIR, SIFT, Feature, Vision programming, Descriptors, D-SIFTAbstract
In this paper a new technique for image retrieval is used. Traditional methods used for image retrieval are not supported for large set of databases. By using the features of the image such as color, shape and texture image can be retrieved efficiently. The Content Based image retrieval (CBIR) technique is the traditional technique used for image retrieval . Several kinds of detectors and descriptors such as SIFT, SURF, FAST, BRIEF, ORB, BRISK, FREAK are used for image retrieval. Among these techniques SIFT is quite powerful. The main drawback of the existing system is that it computes only at the interest points. The proposed system addresses about the D- SIFT algorithm in which the SIFT is computed at every pixel, or every kth pixel. The Density- Scale Invariant Feature Transform (D-SIFT) is a stand out amongst the most locally feature detector and descriptors which is utilized as a part of the majority of the vision programming. The main advantage of using Dense SIFT over SIFT is speed. The main goal is to segment the similar object from the two images. As the result of the proposed system the poorly localized points are removed by key point localization.
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