Auto Classification of Product Images Based on Complementary Features and Class Descriptor

Auto-classification of online goods is a great need for intelligent e-commerce. Some valuable information of product, such as long sleeve T-shirt vs short one,round collar vs V-neck collar, can be tagged based on the image features and classification algorithms. As two complementary features,PHOG (Pyramid Histogram of Orientated Gradients) and PHOW (Pyramid Histogram Of Words) are adopted to extract and describe the features of product-images. An improved nearest-neighbor classifier based on the class descriptor of images is proposed. Experimental results show that the accuracies have been achieved between 70% to 99% on 2 or 3 real-time classification task, which is markedly improved compare to the existent result.