Enlarge Sketch based Image Retrieval by Re-Ranking and Relevance Feedback

Enlarge Sketch based Image Retrieval by Re-Ranking and Relevance Feedback

D.Amrutha, Y.V.V Satyanarayana

D.Amrutha, Y.V.V Satyanarayana "Enlarge Sketch based Image Retrieval by Re-Ranking and Relevance Feedback" Published in International Journal of Trend in Research and Development (IJTRD), ISSN: 2394-9333, Special Issue | NCETC-17 , April 2017, URL: http://www.ijtrd.com/papers/IJTRD7897.pdf

In this paper, Sketch-based image retrieval method utilize a hand-drawn sketch collected of plain stroke or lines to perform the image retrieval mission .In a user’s visual observation, the majority instructive lines in an image are the contour. A sketch is usually a rough narrative of an objects form and contours. The sketch does not be inventive, and is plainly the users rough idea of the planned object .Sketch-based image retrieval often needs to optimize the trade-off between competence and accuracy .Directory structure are usually applied to major database to recognise proficient retrievals .However, the presentation can be pretentious by quantization error .Moreover, the indirectness of user-provided example may also Image retrieval methods. Sketch-based image retrieval systems that maintain the directory structure are tricky. In this paper, I recommend an efficient sketch-based image retrieval approach with re-ranking and relevance feedback scheme. This approach makes full use of the semantics in question sketches and the top ranked imagery of the primary results. I also apply relevance feedback to discover more relevant images for the input question sketch. The combination of the two scheme results in common profit and improve the presentation of sketch-based image retrieval.

Sketch, SBIR, Relevance Feedback, Image Retrieval Contour matching

Special Issue | NCETC-17 , April 2017


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