Scene identification method based on mixed depth structure

Abstract

The invention discloses a scene identification method based on a mixed depth structure. A prior mixed depth identification frame is improved and applied to the task for scene identification, a depth self-encoder is adopted to automatically extract the local image block characteristic to replace the local characteristic extraction layer of a conventional mixed depth network to obtain an image block high-grade local characteristic; at the same time, spatial information is introduced to improve local characteristic coding layer of scene identification, and at the end, the scene is identified via depth discrimination network to improve the mixed depth mixed scene identification frame, so that the improved mixed depth scene is approximate to a convolutional neural network in the aspects of form and identification accuracy and is higher than a depth convolutional neural network in the aspect of calculation efficiency. In addition, the scene data is selectively expanded for the within-class difference and intra-class similarity of scene data, the construction robustness is high, and the method is suitable for the depth mixed scene identification model of a small data set.

Description

technical field

[0001] It involves the fields of image processing and computer vision, especially a scene recognition method based on hybrid deep structure. Background technique

[0002] Scene recognition is an important research direction in the field of computer vision. Scene recognition technology, that is, the computer automatically distinguishes the scene category of the collected images, which helps to deepen the computer's understanding of the scene and assist the computer to make other decisions. This technology is widely used in robot control, remote sensing image processing, intelligent monitoring and other fields. Aiming at the technical difficulties of scene recognition, domestic and foreign researchers have proposed many advanced algorithms.

[0003] Recently, due to the development of computer technology, deep learning technology has achieved great success in the field of computer vision. The supervised deep learning network is composed of a multi-layer nonli...

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