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

Abstract

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 E-commerce fields with various classes. Consequently, a novel Incremental-based Improved YoloV3 with Hyper-parameter 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 threshold value is accomplished to deal the untrained product and the training weight is updated optimally using the proposed PDP-SSA. Finally, the enhanced model is compared over the state-of-the-art classification, provides the expected result for significantly classifying the multiple classes of products. The developed PDP-SSA-II-YoloV3-HO model is also able to perform recommendations in E-commerce sites even when the input is given as images other than text for searching the items. These uploaded images are analyzed in the developed II-Yolov3-HO model for providing the relevant items to the customer based on their features. The developed PDP-SSA-II-YoloV3-HO model achieves 99.46 % regarding accuracy. Throughout the validation, the developed model outperforms enhanced and reliable performance than the existing approaches.