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Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the Semantic Hierarchy Of attributE Convolutional Neural Network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe dataset composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.
This article was published in the following journal.
Name: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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The perceived attribute of a sound which corresponds to the physical attribute of intensity.
A research and development program initiated by the NATIONAL LIBRARY OF MEDICINE to build knowledge sources for the purpose of aiding the development of systems that help health professionals retrieve and integrate biomedical information. The knowledge sources can be used to link disparate information systems to overcome retrieval problems caused by differences in terminology and the scattering of relevant information across many databases. The three knowledge sources are the Metathesaurus, the Semantic Network, and the Specialist Lexicon.
Studies which start with the identification of persons with a disease of interest and a control (comparison, referent) group without the disease. The relationship of an attribute to the disease is examined by comparing diseased and non-diseased persons with regard to the frequency or levels of the attribute in each group.
A family of transcription factors characterized by the presence of a bipartite DNA-binding domain known as the POU domain. The POU domain contains two subdomains, a POU-specific domain and a POU-homeodomain. The POU domain was originally identified as a region of approximately 150 amino acids shared between the Pit-1, Oct-1, Oct-2, and Unc-86 transcription factors.
A conserved protein interaction domain of the death domain superfamily that is structurally similar to the DEATH EFFECTOR DOMAIN and CASPASE RECRUITMENT DOMAIN. Death domains bind each other to form oligomers and occur on DEATH DOMAIN RECEPTORS, where they are required for APOPTOSIS signaling and non-apoptotic functions.