Abstract
Dermatologists consider color as one of the major discriminative aspects of melanoma. In this paper we evaluate the importance of color in the keypoint detection and description steps of the Bag-of-Features model. We compare the performance of gray scale against that of color sampling methods using Harris Laplace detector and its color extensions. Moreover, we compare the performance of SIFT and Color-SIFT patch descriptors. Our results show that color detectors and Color-SIFT perform better and are more discriminative achieving Sensitivity = 85%, Specificity = 87% and Accuracy = 87% in PH2 database [17].
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Barata, C., Marques, J.S., Rozeira, J. (2013). Evaluation of Color Based Keypoints and Features for the Classification of Melanomas Using the Bag-of-Features Model. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2013. Lecture Notes in Computer Science, vol 8033. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41914-0_5
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DOI: https://doi.org/10.1007/978-3-642-41914-0_5
Publisher Name: Springer, Berlin, Heidelberg
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