Multi-label natural scene classification method based on spatial pyramid and sparse coding

Abstract

The invention discloses a multi-label natural scene classification method based on spatial pyramid and sparse coding, and mainly aims at solving the problems that a present classification method cannot completely describe a natural scene and the classification accuracy is relatively low. The method comprises the steps that a multi-label class library of a natural scene is established; the scale invariant feature (SIFT) of the class library is extracted to generate a sparse dictionary D; the sparse dictionary is used to carry out dictionary mapping on the image, and the spatial pyramid and sparse coding are used to generate a multi-scale sparse vector; and a classification result of a multi-class support vector machine is used to correct and order classification results of a support vector machine, and further to obtain the final classification result of the natural scene image. The multi-scale feature, sparse coding and multi-scale classification method is used, local information of the image is extracted, characteristic information of the is enriched, the natural scene is described more comprehensively, the classification precision and robustness of the natural scene are improved, and the method can be used to match, classify and identify the natural scenes.

Description

technical field

[0001] The invention belongs to the technical field of image processing, and in particular relates to a natural scene classification method for image translation, rotation, brightness and scale change, specifically a multi-label classification method for natural scenes based on spatial pyramid sparse coding, which can be used for natural scene matching of images, classification and identification. Background technique

[0002] In the past decade, natural scene image classification has become a very important technical problem in the field of image processing. Natural scene image classification has a wide range of applications, such as target recognition and detection, intelligent vehicle or robot navigation and other fields. Due to the large differences in the categories of natural scene images, differences in lighting conditions, and differences in the scale of the images themselves, the classification of natural scene images is still difficult to deal with...

Images