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Content-Based E-Commerce Image Classification Research
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DOI：10.1109/ACCESS.2020.3018877.png)
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Abstract
En 中文
The 21st century is the era of big data in the Internet. Online shopping has become a trend, and e-commerce has developed rapidly. With the exponential increase of the amount of commodity image data, the management of massive commodity image database restricts the development of e-commerce to some extent. In order to effectively manage goods and improve the accuracy and efficiency of product image retrieval, this paper uses content-based methods to classify e-commerce images. Aiming at the problems of insufficient classification accuracy and long classification training time in e-commerce image classification, an adaptive momentum learning rate based LBP-DBN training algorithm-AML-LBP-DBN and commodity image classification method based on image local feature multi-level clustering and image-class nearest neighbor classifier are proposed. By simulating the commodity identification dataset RPC, the results show that the proposed method has obvious advantages in the classification training time and classification accuracy of e-commerce images.
Keywords:
Image classification
Feature extraction
Training
Classification algorithms
Machine learning
Face recognition
Encoding
E-commerce
commodity image classification
adaptive training
local feature multi-level clustering
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