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Category-partitioned Content Based Image Retrieval for fine-grained objects with feature extraction through Convolution Neural Network and feature reduction through principle component analysis
Category-partitioned Content Based Image Retrieval for fine-grained objects with feature extraction through Convolution Neural Network and feature reduction through principle component analysis
#$%^&*AU2018101525A420181115.pdf#####ABSTRACT Our system is to bridge the semantic gap, with the concept of classes involved in feature extraction and accordingly feature matching. Different from conventional CBIR system, our system is designed to return the most similar images among which belong to the same class, categorized by their brands combined with their types, for example the Cushesandals and Ecco-slipons, which reaches to semantic level. In general, our invention puts forward a new image retrieval system that uses ResNet-50 as the base model to achieve the best retrieval result.
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