Multi-level image representation for large-scale image-based instance retrieval

Abstract

In recent years, instance-level-image retrieval has attracted massive attention. Several researchers proposed that the representations learned by convolutional neural network (CNN) can be used for image retrieval task. In this study, the authors propose an effective feature encoder to extract robust information from CNN. It consists of two main steps: the embedding step and the aggregation step. Moreover, they apply the multi-task loss function to train their model in order to make the training process more effective. Finally, this study proposes a novel representation policy that encodes feature vectors extracted from different layers to capture both local patterns and semantic concepts from deep CNN. They call this ‘multi-level-image representation’, which could further improve the performance. The proposed model is helpful to improve the retrieval performance. For the sake of comprehensively evaluating the performance of their approach, they conducted ablation experiments with various convolutional NN architectures. Furthermore, they apply their approach to a concrete challenge – Alibaba large-scale search challenge. The results show that their model is effective and competitive.

Keywords

• image retrieval
• learning (artificial intelligence)
• feature extraction
• image representation
• feedforward neural nets
• multilevel image representation
• large-scale image
• instance-level-image retrieval
• convolutional neural network
• image retrieval task
• effective feature encoder
• embedding step
• aggregation step
• multitask loss
• novel representation policy
• deep CNN
• multilevel-image representation
• retrieval performance
• Alibaba large-scale search challenge