Towards sketch-based image retrieval with deep cross-modal correlation learning

A novel scheme with deep cross-modal correlation learning is developed in this paper to facilitate more effective Sketch-based Image Retrieval (SBIR) for large-scale annotated images. It integrates the deep multimodal feature generation, deep cross-modal correlation learning and similarity search optimization through mining all the beneficial multimodal information sources in sketches and images, which can be treated as an inter-related correlation distribution over deep representations of sketches and images. Very positive results were obtained in our experiments using a large quantity of public data.