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Fabric image retrieval is beneficial to many applications including clothingsearching, online shopping and cloth modeling. Learning pairwise imagesimilarity is of great importance to an image retrieval task. With theresurgence of Convolutional Neural Networks (CNNs), recent works have achievedsignificant progresses via deep representation learning with metric embedding,which drives similar examples close to each other in a feature space, anddissimilar ones apart from each other. In this paper, we propose a novelembedding method termed focus ranking that can be easily unified into a CNN forjointly learning image representations and metrics in the context offine-grained fabric image retrieval. Focus ranking aims to rank similarexamples higher than all dissimilar ones by penalizing ranking disorders viathe minimization of the overall cost attributed to similar samples being rankedbelow dissimilar ones. At the training stage, training samples are organizedinto focus ranking units for efficient optimization. We build a large-scalefabric image retrieval dataset (FIRD) with about 25,000 images of 4,300fabrics, and test the proposed model on the FIRD dataset. Experimental resultsshow the superiority of the proposed model over existing metric embeddingmodels.

Learning Deep Similarity Models with Focus Ranking for Fabric Image Retrieval