The Deep Learning Of Visual Features In Image Search Applications

The current image retrieval has developed from a single step by step to the development of comprehensive character of the text, content, user orientation and visual demand based on the direction of text or content based retrieval. And as the big data era, a variety of statistical machine learning algorithms can be widely applied principle based on. Especially the use of visual features deep learning and LTR scheduling model based on the image retrieval technology, obtained the unprecedented development.Firstly,we introduce an important application of visual features based on the depth of learning, the basic concept of visual similarity features and related generation algorithm is described in detail, including the optimization scheme of sparse matrix visual features. Then based on the generation of visual similarity characteristics of the whole architecture supports are introduced. And a detailed description of the architecture of the realization of technical points, and strategy algorithm. Finally, demonstrating the practical application of the visual similarity feature in image retrieval system and enhance the retrieval effect brought by the.Then we introduce the other two application of visual feature depth based on learning. The visual similarity of feed back Outlier strategy and vision based text feature depth study of CDNN network. And introduces the concrete steps of the algorithm in subsection two application and increase revenue in the actual retrieval effect. These applications are extending the depth of visual features based on learning, which is based on visual text feature depth study of the CDNN network, more is the method of generating of visual features is extended, the depth of learning the most advanced machine learning idea is applied to the generation scheduling model LTR features.