Feature fusion of dynamic visual Angle images based on grouping feature weight

Home > Archive>Volume 44, Issue 4, 2021 >144-148

Feature fusion of dynamic visual Angle images based on grouping feature weight
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                        Zhang Jiaqi1Zhang Jiaqi
Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai University, Shanghai 200444 China
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Zhang Jinyi1Zhang Jinyi
Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai University, Shanghai 200444 China
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Lou Liangliang2Lou Liangliang
Key Laboratory of Wireless Sensor Network & Communication，Shanghai Institute of Microsystem and Information Technology，Chinese Academy of Sciences，865 Changning Road, Shanghai 200050 China
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Affiliation:1.Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication,Shanghai University, Shanghai 200444 China; 2. Key Laboratory of Wireless Sensor Network & Communication，Shanghai Institute of Microsystem and Information Technology，Chinese Academy of Sciences，865 Changning Road, Shanghai 200050 China
Clc Number:TP391
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Abstract:

In order to solve the problem of low recognition accuracy, the image captured by mobile robot in the process of target recognition has multi-target interference and the target feature is limited in a single perspective. In this paper, a method of Feature fusion of dynamic visual Angle images based on grouping feature weight weights is proposed. In this method, multiple target features are weighted and grouped by progressive K-means clustering, and continuous image features are fused from dynamic perspective by LSTM network, so as to improve the accuracy of target recognition. The verification results show that the first recognition rate on market-1501 data set reaches 93.80%, and the average accuracy reaches 89.13%, with good experimental results.

Key words:grouping feature weight;feature fusion;feature extraction;target recognition;long short term memory network

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Online: November 25,2024
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