Quadruplet Networks for Sketch-Based Image Retrieval

TLDR: This paper uses deep convolutional neural networks (ConvNets) to address sketch-based image retrieval (SBIR) and proposes a new architecture the quadruplet networks which enhance ConvNet features for SBIR, enabling ConvNets to extract more robust global and local features.

Abstract: Freehand sketches are a simple and powerful tool for communication. They are easily recognized across cultures and suitable for various applications. In this paper, we use deep convolutional neural networks (ConvNets) to address sketch-based image retrieval (SBIR). We first train our ConvNets on sketch and image object recognition in a large scale benchmark for SBIR (the sketchy database). We then conduct a comprehensive study of ConvNets features for SBIR, using a kNN similarity search paradigm in the ConvNet feature space. In contrast to recent SBIR works, we propose a new architecture the quadruplet networks which enhance ConvNet features for SBIR. This new architecture enables ConvNets to extract more robust global and local features. We evaluate our approach on three large scale datasets. Our quadruplet networks outperform previous state-of-the-art on two of them by a significant margin and gives competitive results on the third. Our system achieves a recall of 42.16% (at k=1) for the sketchy database (more than 5% improvement), a Kendal score of 43.28Τb on the TU-Berlin SBIR benchmark (close to 6Τb improvement) and a mean average precision (MAP) of 32.16% on Flickr15k (a category level SBIR benchmark).