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.
Image classification is a fundamental task in computer vision, implying a wide range of challenging problems, such as object recognition, scene understanding, and image tagging. One of the most popular approaches to image classification, the bag-of-features (BoF) model, represents an image with a long feature vector and adopts machine learning algorithms for training and testing. Owing to its simplicity...
Codebook plays an important role in the bag-of-visual-words (BoW) model for image classification. However, the traditional codebook generation procedure ignores the spatial information. Although a lot of works have been done to consider the spatial information for codebook generation, most of them rely on fixed region selection or partition of images, hence are not able to cope with the variations...
Recently, dictionary learned by sparse coding has been widely adopted in image classification and has achieved competitive performance. Sparse coding is capable of reducing the reconstruction error in transforming low-level descriptors into compact mid-level features. Nevertheless, dictionary learned by sparse coding does not have the ability to distinguish different classes. That is to say, it is...
Human beings have the ability to learn to recognize a new visual category based on only one or few training examples. Part of this ability might come from the use of knowledge from previous visual experiences. We show that such knowledge can be expressed as a set of ldquouniversalrdquo visual features, which are learned from randomly collected natural scene images. Using these visual features, we...
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.