The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
For large-scale image retrieval, high dimensional features make the retrieval system inefficiency. In this paper, we propose a framework of deep feature hash codes for content-based image retrieval system. In this framework, we firstly extract image features by a pre-trained convolutional neural networks model. Secondly, we use different hashing methods for binary feature extraction. Finally, we use...
For large-scale image retrieval, high-dimensional image representations derived from pre-trained Convolutional Neural Networks (CNNs) make the retrieval system inefficiency. In this paper, we propose to combine nonlinear dimension reduction and hashing method for efficient image retrieval. We firstly extract 4096-dimension features by a pre-trained CNNs model. Secondly, we use t-Distributed Stochastic...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.