A. Pujol and L. Chen (France)
Image Processing, Low level features, Hough Transform
While the problem of Content Based Image Retrieval (CBIR) and automated image indexing systems has been widely studied in the past years they still represent a chal lenging research field. Indeed capturing high level seman tics from digital images basing on low level basic descrip tors remains an issue. A review of existing systems shows that edge descriptors are among the most popular features. While color features have led to extensive work, edge fea tures haven’t produced such active research and most cur rent systems rather rely on completing basic edge informa tion with other, more computationally expensive features such as texture. In this paper we propose to work on a more accurate edge feature while keeping a relatively low computation cost. We will begin with a review of common edge features used in CBIR and automated indexing sys tems, we will then explain our Enhanced Fast Hough Trans form algorithm and the edge descriptor we derived from it. Through a study of computational complexity, we will ex plain that the computational burden is kept minimal and ex perimental results using a sample automated indexing sys tem will show that our new edge feature significantly im proves over more traditional descriptors.
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