An Indoor Scene Recognition Method Combining Deep Learning and Sparse Representation

Abstract

The invention discloses an indoor scene recognition method combining deep learning and sparse representation. Discriminate and detect object categories with test samples to construct the bottom-level features of each indoor scene image; use the bag-of-words model to combine the bottom-level features and spatial features of each indoor scene image to construct mid-level features; The middle-level features are combined to construct a sparse dictionary; the sparse dictionary is used to sparsely represent the test samples, and the residual is calculated according to the obtained sparse solution and the input test sample, and the object category of the test sample is judged according to the size of the residual ; It will be judged to get the output of the object category it belongs to. The invention can accurately identify indoor scenes, can effectively improve the accuracy and robustness of indoor scene identification, and has high practical performance.

Description

technical field [0001] The invention relates to an indoor scene recognition method combined with deep learning and sparse representation, and belongs to the technical field of image processing technology. Background technique [0002] With the development and popularization of information technology and intelligent robots, scene recognition, as an important research content, has become an important research issue in the field of computer vision and pattern recognition. Scene image classification is the automatic classification of image datasets according to a given set of semantic labels. The scene recognition model is mainly divided into three major blocks: based on low-level features, based on mid-level features, and based on visual vocabulary. The so-called low-level features are to extract the global or block texture, color and other features of the scene image to classify the scene image, such as the research of Valiaya and Szumme et al., but this method of extracting ...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, provide an indoor scene recognition method that combines deep learning and sparse representation, and solve the problems of small differences, occlusion, scale, and angle changes due to the current indoor scene intra-category differences. The current indoor scene recognition is more complex and difficult than the outdoor scene recognition, so the recognition effect is poor, so as to improve the recognition rate and robustness of the indoor scene recognition algorithm.

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