Improved characteristic convolutional neural network image identification method

Abstract

The invention discloses an improved characteristic convolutional neural network image identification method. According to the improved characteristic convolutional neural network image identification method, firstly, pre-processing on an image to be inputted is carried out; secondly, a characteristic extraction layer is added to a convolutional neural network structure, and the characteristics are amplified; image data with characteristic amplification is inputted to a convolutional neural network, convolutional operation learning identification for the characteristics in the image is carried out, output of the convolutional neural network is acquired, and offset operation on the image after pre-processing in the first step is further carried out to acquire textural characteristics of the image; lastly, the textural characteristics are analyzed to output a result, and outputs of the convolutional neural network are fused to output a final result. By adding the characteristic extraction layer, the characteristics of the image are reinforced, so extraction of some unconspicuous characteristics is facilitated, the image identification rate can be effectively improved, and methods can be further provided for image searching and image identification, and identification efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, in particular to an image recognition method based on an improved feature convolutional neural network. Background technique [0002] Currently, pattern recognition is still one of the hottest research directions in computer science. Giving computers sensory capabilities is a research hotspot in pattern recognition. Scholars have proposed many algorithms for graphics and image processing, speech processing and other directions. [0003] In terms of image recognition, it is mainly to extract feature points from the image, and then use this feature to identify the image. At present, there are many main algorithms used for image recognition, such as: wavelet analysis, support vector machine (SVM), genetic algorithm, neural network algorithm, etc. [0004] Heisele, Bernd proposed a face recognition algorithm based on SVM. It divides the human face into multiple areas, uses the facial ...

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Problems solved by technology

However, due to the relatively high complexity of fonts, the algorithm training time is relatively long, and achieved certain results (Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. "Gradient-based Learning Applied to Document Recognition", Proceedings of the IEEE, vol.86, pp.2278–2324, November 1998.)

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