54 results about "Contextual image classification" patented technology

A method for improving scene classification of a sequence of digital images is disclosed herein. Such a method may include providing a sequence of images captured in temporal succession; (b) classifying each of the images individually based on information contained in the image alone to generate a first image classification; and (c) imposing a pre-determined temporal context model on the sequence of images to generate a final image classification for each image in the sequence.

Systems, methods and computer readable media are disclosed for improving compression efficiency and quality in a remote session via tile image classification and variable encoding. A server determines a set of codecs that are shared by both the server and a corresponding client. Then, when it receives an image, it determines whether classification of the image is required. Where classification of the image is not required, the server sends the client the image, either uncompressed or compressed with a default codec and default fidelity. Where classification of the image is required, the server classifies the image (e.g. the image comprises either text or photograph), and based on that classification determines a codec with which to encode the image, and a fidelity to use on the encoding. The server performs that encoding with the codec and the fidelity, and then sends this encoded image to the client.

The invention discloses an SAR image classification method based on united sparse representation. The SAR image classification effect is improved based on an existing sparse representation method. According to the implementation process, the SAR image classification method comprises the steps that (1) SAR images to be trained are input, the features of the SAR images are extracted, and similar sets are classified; (2) united sparse representation is conducted on the similar set of each class of the SAR images, and a small dictionary and sparse coefficients of each similar set are obtained correspondingly; (4) SAR images to be tested are input, the features of the SAR images to be tested are extracted, feature vectors are projected on the small dictionary, and coefficients of the tested images are obtained; (5) the coefficients of the tested images and the sparse coefficients of all the trained images are matched, a set of most matched coefficients in the sparse coefficients are found, and the marked category of the set of most matched coefficients serves as the category of the SAR images to be tested. Compared with a traditional KNN and classic sparse representation classification method, the accuracy of even texture image and SAR image classification is greatly improved.