Part-based deep representation for product tagging and search

Chen, Keqing

doi:10.1117/12.2280300

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Several researchers focused on product classification techniques in e-commerce applications. Chen [11] developed a visual-based image classifier model for the classification of product tagging. It explores consumer product feasibility using image classification.…”

Section: Related Workmentioning

confidence: 99%

A Novel Autoregressive Co-Variance Matrix and Gabor Filter Ensemble Convolutional Neural Network (ARCM-GF-E-CNN) Model for E-Commerce Product Classification

Latha¹,

Rao²

2022

RIA

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With the advancement of technologies intelligent and automated environments are rapidly evolved with deep learning and transfer learning techniques. However, the existing technique exhibits different difficulties due to increases in processing data complexity. This research developed an Artificial Intelligence (AI) framework for e-commerce product classification. The data for analysis is collected from different website sources and images are classified. The proposed AI framework is stated as Autoregressive Co-Variance Matrix and Gabor Filter Ensemble Convolutional Neural Network (ARCM-GF-E-CNN). The ARCM-GF-E-CNN incorporates an auto-regressive Co-variance matrix for the classification of online product images. The collected database is categorized into class based on features of the image. The simulation results expressed that the proposed ARCM-GF-E-CNN exhibits higher accuracy for the validation and testing dataset. Further, the analysis of ARCM-GF-E-CNN with existing technique expressed that the proposed classifier increases accuracy, precision, recall, and F1-score.

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Section: Related Workmentioning

confidence: 99%