Large-scale product classification via spatial attention based CNN learning and multi-class regression

Abstract

Large-scale product classification is an essential technique for better product understanding. It can provide support to online retailers from a number of aspects. This paper discusses the CNN based product classification with the existence of class hierarchy. A SaCNN-MCR method is developed to settle this task. It decomposes the classification into two stages. Firstly, a spatial attention based CNN model that directly classifies a product to leaf classes is proposed. Compared with traditional CNNs, the proposed model focuses more on product region rather than the whole image. Secondly, the outputted CNN score together with class hierarchy clues are jointly optimized by employing a multi-class regression (MCR) based refinement, which provides another kind of data fitting that further benefits the classification. Experiments on nearly one million real-world product images show that, based on the two innovations, SaCNN-MCR steadily improves the classification performance over CNN models without these modules. Moreover, it is demonstrated that CNN features characterize product images much better than traditional features, whose clas- sification performance outperforms those of the traditional features by a large margin.