Artificial Intelligence Recognition Simulation of 3D Multimedia Visual Image Based on Sparse Representation Algorithm
| Autor: | Weixiao Chen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
General Computer Science
Multimedia search Pixel Multimedia Computer science General Engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-invariant feature transform Sparse approximation computer.software_genre Matching pursuit algorithm simulation image recognition K-SVD Robustness (computer science) Feature (computer vision) Computer Science::Computer Vision and Pattern Recognition General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering stereo matching Algorithm Feature learning computer Sparse representation lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 8, Pp 121437-121450 (2020) |
| ISSN: | 2169-3536 |
| Popis: | With the rapid development of computer networks and multimedia technologies, images, which are important carriers of information dissemination, have made human cognition of things easier. Image recognition is a basic research task in computer vision, multimedia search, image understanding and other fields. This paper proposes a hierarchical feature learning structure that is completely automatically based on the original pixels of the image, and uses the K-SVD (K-Singular Value Decomposition) algorithm with label consistency constraints to train the discriminant dictionary. For different types of image data sets, the algorithm only extracts image blocks. After dense sampling, an efficient OMP (Orthogonal Matching Pursuit) encoder is used to obtain a layered sparse representation. The improved SIFT (Scale Invariant Feature Transform) algorithm is used to solve the difficult problem of multimedia visual image stereo matching. The feature point extraction and stereo matching of multimedia visual images, different scales and different viewpoint images are analyzed separately. Aiming at a large number of low-dimensional geometric features of 3D images, this paper studies the extraction and sorting strategies of low-dimensional geometric features of 3D images. A sparse representation method for 3D images is proposed, and the sparseness of image features is evaluated. This further improves the accuracy of 3D image representation and the robustness of 3D image recognition algorithms. |
| Databáze: | OpenAIRE |
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