An Improved Histogram of Edge Local Orientations for Sketch-Based Image Retrieval
Content-based image retrieval requires a natural image (e.g, a photo) as query, but the absence of such a query image is usually the reason for a search. An easy way to express the user query is using a line-based hand-drawing, a sketch, leading to the sketch-based image retrieval. Few authors have addressed image retrieval based on a sketch as query, and the current approaches still keep low performance under scale, translation, and rotation transformations. In this paper, we describe a method based on computing efficiently a histogram of edge local orientations that we call HELO. Our method is based on a strategy applied in the context of fingerprint processing. This descriptor is invariant to scale and translation transformations. To tackle the rotation problem, we apply two normalization processes, one using principal component analysis and the other using polar coordinates. Finally, we linearly combine two distance measures. Our results show that HELO significantly increases the retrieval effectiveness in comparison with the state of the art.
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