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Incremental-based YoloV3 model with hyper-parameter optimization for product image classification in E-commerce sector

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APPLIED SOFT COMPUTING 2024年 165卷

作者： Dutta, Munmi Ganguly, Amrita Assam Engn Coll Dept Elect Engn Gauhati 781013 Assam India

Over the past few years, the E-commerce industry has grown tremendously for selling products to consumers. Here, the consumer can easily purchase the products from their residing seats and gets the products at the door step. Also, the image quality is suggested and also the image classification is intruded to meet the consumer prerequisite. Most of the image classification models are developed by machine learning approaches to improve the reliability, scalability, and accuracy level. Due to the heterogeneous nature of images, it restricts the features and lengthy dimensions for datasets which it degrades the performance whereas the classification of products becomes a cumbersome situation. In order to resolve the problem raised by existing methods, a novel product image classification model is proposed for the fashion E-commerce sector. The prime aspect of the proposed model is to build an effective E-commerce sector for a layman, where every individual can purchase the products appropriately. Initially, the dataset is constructed with diverse product images that are obtained from different Ecommerce fields with various classes. Consequently, a novel Incremental-based Improved YoloV3 with Hyperparameter Optimization (II-Yolov3-HO) model is introduced to enhance the classification performance. In the proposed model, some of the hyper parameters like convolution and pooling layers are replaced with the Visual Geometry Group16 (VGG16) model, the remaining convolutional layers are enhanced by the atrous convolution operation, and pooling layers are modified with Atrous Spatial Pyramid Pooling (ASPP). In entire architecture, the weight factor is optimized using predator-based squirrel search algorithm (PDP-SSA) algorithm. Thus, the new E-commerce sector becomes free to categorize the products, and if it deals with a new product, it is declared as unknown data, which is then trained again in the proposed model. Hence, the concept of classification score matching with the

关键词： Product image classification Fashion E -commerce sector Unknown classes hyper-parameter optimization predator-based squirrel search algorithm

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