Visual content-based web page categorization with deep transfer learning and metric learning

Abstract The growing amounts of online multimedia content challenge the current search, recommendation and information retrieval systems. Information in the form of visual elements is highly valuable in a range of web mining tasks. However, the mining of these resources is a difficult task due to the complexity and variability of images, and the cost of collecting big enough datasets to successfully train accurate deep learning models. This paper proposes a novel framework for the categorization of web pages on the basis of their visual content. This is achieved by exploring the joint application of a transfer learning strategy and metric learning techniques to build a Deep Convolutional Neural Network (DCNN) for feature extraction, even when training data is scarce. The obtained experimental results evidence that the proposed approach outperforms the state-of-the-art handcrafted image descriptors and achieves a high categorization accuracy. In addition, we address the problem of over-time learning, so the proposed framework can learn to identify new web page categories as new labeled images are provided at test time. As a result, prior knowledge of the complete set of possible web categories is not necessary in the initial training phase.