An improved bag of dense features for skin lesion recognition

Abstract

Skin is the largest and fastest growing organ in the human body. There are various types of skin lesions in which malignancy are non-invasively detected and recognized based on their local and global attributes of the image using an image-guided system. In this work, Gradient Location and Orientation Histogram and color features are fused together to construct the Inherently Hybrid Image Descriptor for skin lesion classification. The features obtained from these descriptor are combined to form a bag of visual words. The improved bag is used to categorize the skin lesion classes as malignant or benign using Support Vector Machine. The performance of the proposed method has been found considerably better than the current state-of-art. It also simplifies the process of diagnosis for undeclared abnormalities in the skin region.

Keywords

• Gradient Location and Orientation Histogram (GLOH)
• Hybrid Image Descriptor (IHID)
• Bag of visual word (BoVW)
• Scale Invariant Feature Transform (SIFT)
• Support Vector Machine (SVM)