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With the explosive increase of online images, fast similarity search is increasingly critical for large scale image retrieval. Several hashing methods have been proposed to accelerate image retrieval, a promising way is semantic hashing which designs compact binary codes for a large number of images so that semantically similar images are mapped to similar(More)
In this paper, we propose a novel Deep Localized Makeup Transfer Network to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face. Given a before-makeup face, her most suitable makeup is determined automatically. Then, both the before-makeup and the reference faces are fed into the proposed Deep Transfer Network(More)
Digital fingerprinting is a promising approach to protect multimedia contents from unauthorized redistribution. Whereas, large scale and high dimensionality make existing fingerprint detection methods fail to trace the traitors efficiently. To handle this problem, we propose a novel local and global structure preserving hashing to conduct fast fingerprint(More)
Digital fingerprinting is a promising approach to protect multimedia content from unauthorized redistribution. However, the existing fingerprints are unsuitable for social network tasks, because they fail to represent the social network structure, which incurs inefficient fingerprint coding. In addition, they are infeasible to efficiently trace colluders(More)
Recently the methods based on visual words have become very popular in near- duplicate retrieval and content identification. However, obtaining the visual vocabulary by quantization is very time-consuming and unscalable to large databases. In this paper, we propose a fast feature aggregating method for image representation which uses machine learning based(More)
In this demo, we present a Beauty eMakeup System to automatically recommend the most suitable makeup for a female and synthesis the makeup on her face. Given a before-makeup face, her most suitable makeup is determined automatically. Then, both the before-makeup and the reference faces are fed into the proposed Deep Transfer Network to generate the(More)
Content-Based large-scale image retrieval has recently attracted considerable attention because of the explosive increase of online images. Inspired by recent advances in convolutional neural networks, we propose a hierarchical deep semantic method for learning similarity function that solves the problems of precision and speed of retrieval in the setting(More)
Recently, researches on content based image copy detection mainly focus on robust feature extraction. However, due to the exponential growth of online images, it is time-consuming and unscalable to search among large scale images. Although many hashing methods has been proposed to improve the efficiency of image copy detection, they confront semantic loss(More)
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