Abstract
In this paper, Bag-of-visual-words (BoVW) model with Speed up robust features (SURF) and spatial augmented color features for image classification is proposed. In BOVW model image is designated as vector of features occurrence count. This model ignores spatial information amongst patches, and SURF Feature descriptor is relevant to gray images only. As spatial layout of the extracted feature is important and color is a vital feature for image recognition, in this paper local color layout feature is augmented with SURF feature. Feature space is quantized using K-means clustering for feature reduction in constructing visual vocabulary. Histogram of visual word occurrence is then obtained which is applied to multiclass SVM classifier. Experimental results show that accuracy is improved with the proposed method.
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