Abstract
This paper proposes a new image descriptor based on color spatial distribution for image similarity comparison. It is similar to methods based on HOG and spatial pyramid but in contrast to them operates on colors and color directions instead of oriented gradients. The presented method assumes using two types of descriptors. The first one is used to describe segments of similar color and the second sub-descriptor describes connections between different adjacent segments. By this means we gain the ability to describe image parts in a more complex way as is in the case of the histogram of oriented gradients (HOG) algorithm but more general as is in the case of keypoint-based methods such as SURF or SIFT. Moreover, in comparison to the keypoint-based methods, the proposed descriptor is less memory demanding and needs only a single step of image data processing. Descriptor comparing is more complicated but allows for descriptor ordering and for avoiding some unnecessary comparison operations.
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This work was supported by the Polish National Science Centre (NCN) within project number DEC-2011/01/D/ST6/06957.
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Najgebauer, P., Korytkowski, M., Barranco, C.D., Scherer, R. (2016). Novel Image Descriptor Based on Color Spatial Distribution. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_63
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DOI: https://doi.org/10.1007/978-3-319-39384-1_63
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