Image classification method based on RGB-D fusion feature and sparse coding

Abstract

The invention discloses an image classification method based on an RGB-D fusion feature and sparse coding. The method comprises following steps of (1) extracting dense SIFT features and PHOG features of a color image and a depth image; (2) carrying out feature fusion on the extracted features of the images by use of a linear serial connection form so as to obtain four kinds of different fusion features finally; (3) using the K-means++ clustering method to carry out clustering processing on the different fusion features so as to obtain four kinds of different vision dictionaries; (4) carrying out local restriction linear coding on each vision dictionary to obtain different image expressing sets; and (5) using the linear SVM to classify the different image expressing sets and using a vote decision method to decide final classification conditions of the obtained classification results. According to the invention, the method is high in classification precision.

Description

technical field [0001] The invention relates to technical fields such as computer vision and pattern recognition, and in particular to an image classification method based on RGB-D fusion features and sparse coding. Background technique [0002] Today's society is an era of information explosion. In addition to a large amount of text information, the multimedia information (pictures, videos, etc.) that human beings come into contact with is also growing explosively. In order to utilize, manage and retrieve images accurately and efficiently, it is necessary for the computer to understand the image content accurately in the way humans understand. Image classification is an important way to solve the problem of image understanding, and it plays an important role in promoting the development of multimedia retrieval technology. The acquired images may be affected by many factors such as viewpoint changes, lighting, occlusion, and background, which makes image classification a ch...

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Problems solved by technology

However, this type of algorithm also has certain defects, that is, the features extracted from the RGB image and the depth image are a single feature, and when a single feature is used, there is insufficient information extraction from the image, and the obtained fusion features cannot be obtained. The reason for fully expressing the image content is that the RGB image is easily affected by illumination changes, viewing angle changes, image geometric deformation, shadows and occlusions, etc., and the depth image is easily affected by the imaging device, resulting in holes, noise, etc. in the image. The problem is that a single image feature extraction cannot maintain robustness to all factors in the image, which will inevitably lose the information in the image

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