Towards a unified view on image quality assessment


Towards a unified view on image quality assessment – The paper presents the first unified technique for image compression that can effectively remove the need to memorize feature vectors from a huge number of feature vectors for image compression. In particular, the algorithm uses a two stage convolutional network with a shared convolutional activation network with a different set of convolutions to extract the best image. The activation network is fed to a new feature detector that optimizes the features extracted from the feature vectors captured by the convolutional activator network. The method is implemented on top of ImageNet, and provides a scalable framework to improve the compression rates of image compression through feature clustering. Experiments on the COCO benchmark show the algorithm can effectively remove feature vectors from a large number of image samples and outperforms other methods.

As the Web continues to evolve and evolve in unprecedented ways, and as people consume and interact with images and videos each day, the Web has become a powerful tool for the analysis of social interactions. We aim and conduct a real-time visual search for a common visual pattern of images and videos, and perform this search with a knowledge of what information in these images and videos are shared with each other using the web. We compare some approaches and show that visual search can be used to find related visual patterns, and present preliminary results to evaluate visual search techniques such as visual similarity and similarity discovery.

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Towards a unified view on image quality assessment

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  • Adaptive Stochastic Learning

    Learning to Find and Recommend Similarities Across Images and VideosAs the Web continues to evolve and evolve in unprecedented ways, and as people consume and interact with images and videos each day, the Web has become a powerful tool for the analysis of social interactions. We aim and conduct a real-time visual search for a common visual pattern of images and videos, and perform this search with a knowledge of what information in these images and videos are shared with each other using the web. We compare some approaches and show that visual search can be used to find related visual patterns, and present preliminary results to evaluate visual search techniques such as visual similarity and similarity discovery.


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