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Inferring the aesthetic quality of images is a challenging computer vision task due to its subjective and conceptual nature. Most image aesthetics evaluation approaches focused on designing handcrafted features, and only a few adopted learning of relevant and imperative characteristics in a data-driven manner. In this paper, we propose to attune Convolutional Neural Networks (CNNs) for image aesthetics...
Approximate nearest neighbor search is a good method for large-scale image retrieval. We put forward an effective deep learning framework to generate binary hash codes for fast image retrieval after knowing the recent benefits of convolutional neural networks (CNNs). Our concept is that we can learn binary codes by using a hidden layer to present the latent concepts dominating the class labels when...
Though it has cost great research efforts for decades, object recognition is still a challenging problem. Traditional methods based on machine learning or computer vision are still in the stage of tackling hundreds of object categories. In recent years, non-parametric approaches have demonstrated great success, which understand the content of an image by propagating labels of its similar images in...
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