Research Of Product Image Classification Methods Based On Content

The aim of content-based image classification is to implement semantic classification automatically based on the visual features. However, some adverse effects, such as the within-class variation, obscure, pose variation, and background interference, are hard to overcome. Therefore it is still a challenging problem in the field of computer vision. Automatic product image classification can effectively improve the overall effectiveness of the E-commerce market, such as quick product querying, determining the placement strategy and conducting product intelligent recommendation. Consequently, it is a critical requirement of E-commerce intelligent. This dissertation focuses on the content-based product image classification with discriminative classification model. The main research work is as follows:(1) For the service of automatic real-time online product classification with some specific information of interest (such as round or pointed of lady shoes, round or V-neckline of T-shirts, etc.) or product categories, the product classification schemes are developed based on the class-specific descriptors and image-to-class nearest neighbor classifiers, in which each product image category is modeled statistically, the category nearest to the query product image in the feature space is chosen as the final classification result. Two kinds of approaches are proposed for class descriptor construction and image-to-class nearest neighbor classification:①Global feature based schemes. With two global complimentary features PHOG (Pyramid Histogram Of Gradient) and PHOW (Pyramid Histogram Of visual Words), CDDP (Class-specific Descriptor with Distribution Parameter) scheme and CDHFM (Class-specific Descriptor with Hierarchical Feature Matching) scheme are constructed, respectively. The image-to-class distances are calculated between the descriptors of the test product image and each class-specific descriptor for automatic product image classification. The procedure is simple and the classification performances are prior to the relative literature.②Local feature based scheme. In this scheme, all the product images and image classes are regarded as orderless sets of local descriptors and image-to-class nearest neighbour classifier is employed for product image classification. Local feature descriptors of each category are hierarchically clustered to speed the calculation of image-to-class distances, and the trade-off between classification accuracy and speed can be achieved flexibly through the set of clustering level numbers and the class filter ratio. (2) To construct the class-specific descriptor, the labeled samples are required to be in a quantity sufficient for good performance. In the case of product classification with a small number of labeled samples, data-driven kernel building methods are explored and a Weighted Quadratic Chi-squared (WQC) histogram kernel function is designed to combine with BOW (Bag Of Word) model. With the kernel based support vector machines, the proposed histogram kernel function offers superior performances with small training samples.(3) Taking into account the complexity of product image classification, such as the big number of categories, large within-class variation, multiple classification bases, multiple features combination methods are designed to boost classification performances.①Multiple kernels combination. To avoid the tedious and difficult joint optimization process, a (decentralized) kernel empirical aligment based scheme is proposed.②Multiple classifier combination. A framework is built with decision-level fusion of heterogeneous strong classifiers, and a scheme of two-layer SVM classifiers cascading is proposed for product image classification. The proposed multiple kernel and multi-classifier combination methods can take the advantage of the complementary features, and perform much better than the traditional combination methods for product image classification.