Fine-Grained Sketch-Based Image Retrieval by Matching Deformable Part Models

An important characteristic of sketches, compared with text, rests with their ability to intrinsically capture object appearance and structure. Nonetheless, akin to traditional text-based image retrieval, conventional sketch-based image retrieval (SBIR) principally focuses on retrieving images of the same category, neglecting the fine-grained character-istics of sketches. In this paper, we advocate the expressiveness of sketches and examine their efficacy under a novel fine-grained SBIR framework. In particular, we study how sketches enable fine-grained retrieval within object categories. Key to this problem is introducing a mid-level sketch representation that not only captures object pose, but also possesses the ability to traverse sketch and image domains. Specifically, we learn de-formable part-based model (DPM) as a mid-level representation to discover and encode the various poses in sketch and image domains independently, after which graph match-ing is performed on DPMs to establish pose correspondences across the two domains. We further propose an SBIR dataset that covers the unique aspects of fine-grained SBIR. Through in-depth experiments, we demonstrate the superior performance of our SBIR framework, and showcase its unique ability in fine-grained retrieval.