Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network
This addresses sketch-based retrieval for applications like fashion and design, though it is incremental as it builds on existing Siamese and triplet loss approaches.
The paper tackles instance-level sketch-based image retrieval by proposing DeepTCNet, a deep triplet classification Siamese network that eliminates the need for pre-training, edge maps, multi-cropping, and sketch augmentation used in prior methods. It achieves state-of-the-art results on five benchmark datasets and introduces a new hairstyle photo-sketch dataset with 3600 images and 2400 pairs.
Sketch has been employed as an effective communicative tool to express the abstract and intuitive meanings of object. Recognizing the free-hand sketch drawing is extremely useful in many real-world applications. While content-based sketch recognition has been studied for several decades, the instance-level Sketch-Based Image Retrieval (SBIR) tasks have attracted significant research attention recently. The existing datasets such as QMUL-Chair and QMUL-Shoe, focus on the retrieval tasks of chairs and shoes. However, there are several key limitations in previous instance-level SBIR works. The state-of-the-art works have to heavily rely on the pre-training process, quality of edge maps, multi-cropping testing strategy, and augmenting sketch images. To efficiently solve the instance-level SBIR, we propose a new Deep Triplet Classification Siamese Network (DeepTCNet) which employs DenseNet-169 as the basic feature extractor and is optimized by the triplet loss and classification loss. Critically, our proposed DeepTCNet can break the limitations existed in previous works. The extensive experiments on five benchmark sketch datasets validate the effectiveness of the proposed model. Additionally, to study the tasks of sketch-based hairstyle retrieval, this paper contributes a new instance-level photo-sketch dataset - Hairstyle Photo-Sketch dataset, which is composed of 3600 sketches and photos, and 2400 sketch-photo pairs.