Abstract: A video classification method and apparatus are provided in embodiments of the present invention. The method includes: establishing a neural network classification model according to a relationship between features of video samples and a semantic relationship of the video samples; obtaining a feature combination of a to-be-classified video file; and classifying the to-be-classified video file by using the neural network classification model and the feature combination of the to-be-classified video file The neural network classification model is established according to the relationship between the features of the video samples and the semantic relationship of the video samples, and the relationship between the features and the semantic relationship are fully considered. Therefore, video classification accuracy are improved.

Abstract: The present invention provides a method and an apparatus for detecting a salient region of an image. Classification processing is obtained by means of pre-training, so as to obtain a classification label, where the classification label is used to indicate a salience detection algorithm for detecting a salient region of the test image. Salience detection is performed on the test image by using the salience detection algorithm indicated by the classification label, so as to obtain the salient region of the test image. Because a salience detection algorithm with the best detection effect is acquired by using the image feature vector of the test image, to detect the salient region of the test image, accuracy of salience detection is improved.

Abstract: The present invention provides a method and an apparatus for detecting a salient region of an image. Classification processing is performed on a test image according to an image feature vector of the test image by using a classifier obtained by means of pre-training, so as to obtain a classification label, where the classification label is used to indicate a salience detection algorithm for detecting a salient region of the test image. Salience detection is performed on the test image by using the salience detection algorithm indicated by the classification label, so as to obtain the salient region of the test image. Because a salience detection algorithm with the best detection effect is acquired by using the image feature vector of the test image, to detect the salient region of the test image, accuracy of salience detection is improved.