Large-Scale E-Commerce Image Retrieval with Top-Weighted Convolutional Neural Networks

Several recent researches have shown that image features produced by Convolutional Neural Networks (CNNs) provide the state-of-the-art performance for image classification and retrieval. Moreover, some researchers have found that the features extracted from the deep convolutional layers of CNNs perform better than that from the fully-connected layers. Features extracted from the convolutional layers have a natural interpretation: descriptors of local image regions correspond well to the receptive fields of the particular features. In order to obtain both representative and discriminative descriptors for large-scale e-commerce image retrieval, we come up with a new feature extraction framework. At first, we propose the Top-Weight method to detect the interesting area of e-commerce images automatically. With the estimated weight, we then aggregate local deep features and produce high-quality global representation for e-commerce image retrieval. We have conducted experiments on an e-commerce dataset ALISC [1] released by Alibaba Group. Experimental results show that our method outperforms other deep learning based methods.