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Efficient contour match kernel
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DOI：10.1016/j.imavis.2018.04.006.png)
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Abstract
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We propose a novel concept of asymmetric feature maps (AFM), which allows to evaluate multiple kernels between a query and database entries without increasing the memory requirements. To demonstrate the advantages of the AFM method, we derive an efficient contour match kernel - short vector image representation that, due to asymmetric feature maps, supports efficient scale and translation invariant sketch-based image retrieval. Unlike most of the short-code based retrieval systems, the proposed method provides the query localization in the retrieved image. The efficiency of the search is boosted by approximating a 2D translation search via trigonometric polynomial of scores by 1D projections. The projections are a special case of AFM. An order of magnitude speed-up is achieved compared to traditional trigonometric polynomials. The results are boosted by an image-based average query expansion approach and, without any learning, significantly outperform the state-of-the-art hand-crafted descriptors on standard benchmarks. Our method competes well with recent CNN-based approaches that require large amounts of labeled sketches, images and sketch-image pairs. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Sketch-based image retrieval
Efficient contour matching
Kernel descriptors
Asymmetric feature maps
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