Image Recognition Method Based on Human Visual Perception

Abstract

The invention discloses an image recognition method based on human vision. This method constructs an image recognition structure capable of cross-problem recognition based on deep learning and human vision. Using this model structure, one model can recognize images in multiple problem domains, which is a further simulation of the human visual system. This method uses the nature of human visual perception, that is, the HMAX method, to directly extract the features of the original image, which reduces the complicated preprocessing steps and improves the calculation efficiency and feasibility of the method. The SDA method reduces the number of parameters in deep learning and improves the generality of the algorithm. To improve the training performance of general forward BP. From the actual experimental results, the classification accuracy of this method is also higher than other classification methods. Therefore, this method is an efficient and feasible image recognition method, and has universal applicability in the field of image recognition.

Description

technical field [0001] The invention relates to pattern recognition, artificial intelligence, computer vision, and stacked autoencoders. In particular, it relates to the object feature extraction model HMAX based on feature combination and the stacked autoencoder SDA under the deep learning model. Background technique [0002] Accurate image recognition has very important research significance. Image recognition technology plays an important role in many aspects such as medicine, aerospace, military, industry and agriculture. Most of the current image recognition methods use manual feature extraction, which is not only time-consuming and laborious, but also difficult to extract. Since the renaissance of deep learning, it has become a part of state-of-the-art systems in different disciplines, especially in computer vision. At present, the form of deep neural network has been proved to be almost the best structure in deep learning structure. [0003] Deep learning is a kind...

AI Technical Summary

Problems solved by technology

[0012] d) The same preprocessing method is not universal in different scenarios, resulting in low recognition accuracy of the same method in different scenarios

[0013] 2. The classic classification method based on human perception (eg, DBN) has too many training parameters

And the optimization process of the optimal result of an ultra-high-dimensional parameter is a rather complicated process.

This increases the complexity of the method used and reduces the computational performance of the algorithm

[0014] 3. Most of the current methods need to label the original image target area or background area

This process requires a large number of calculations and manual operations, which is not very practical

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