Artificial intelligence (AI) in image processing

Artificial intelligence (AI) in image processing

Efficient use of unstructured data from large amounts of image and voice data has always been a challenge for data mining professionals. Processing unstructured data often involves the use of deep learning algorithms, and these algorithms can be daunting for beginners. In addition, processing unstructured data often requires a powerful GPU and a large amount of computing resources. This article introduces an image recognition method using deep learning. This method can be applied to situations such as illegal image filtering and object detection.

This experiment generates an image recognition model using the TensorFlow deep learning framework in Alibaba’s Cloud Machine Learning Platform for AI. The whole procedure takes about 30 minutes to complete. After training the model, the system was able to recognize the bird in the following image and return the word “bird”:

 

 

This test can be generated from the following TensorFlow image classifier template:

Abstract

As the main means of human communication and understanding of the world, images are one of the important sources of information of human intellectual activity. With the development of the times, the demand for image processing technology is increasing day by day. The rapid development of computer technology also laid the foundation for the application of image processing. To achieve better image processing efficiency, this paper focuses on the application of artificial intelligence algorithms in image processing. Image segmentation is a technology that decomposes images into regions with different characteristics and extracts useful targets. This can be considered as a combinatorial optimization problem. It is possible to apply artificial intelligence algorithms to optimization problems. First, this paper introduces the ant colony algorithm in the artificial intelligence algorithm, builds the basic principle and mathematical model of the ant colony algorithm. Second, in order to improve the global search ability of the ant colony algorithm, this paper introduces the fish crowding function into the ant colony algorithm. Finally, the improved ant colony algorithm is used in image segmentation to improve the efficiency of image segmentation. Simulation results show that using ant colony algorithm in image segmentation is feasible. And the improvement of ant colony algorithm optimization is effective. The improved ant colony algorithm applied in image segmentation can greatly improve segmentation performance hiệu.

 

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