Product Image Classification Techniques
Dhaval Wagh1, Smita Mahajan2
1Dhaval Wagh, Department of Computer Science and Engineering, Symbiosis Institute of Technology, Pune, India.
2Smita Mahajan, Department of Computer Science and Engineering, Symbiosis Institute of Technology, Pune, India.
Manuscript received on 04 April 2019 | Revised Manuscript received on 11 April 2019 | Manuscript Published on 26 April 2019 | PP: 389-393 | Volume-8 Issue-6S April 2019 | Retrieval Number: F60820486S19/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The excessive amount of e-commerce products within the last few years has become a serious problem for shoppers when searching for relevant product information. This led to the emergence of a recommendation technology that has the capability to discover relevant shopping products that meet the user’s preferences. Classification is a machine learning technique that could assist in increasing scalability, creating dynamic user profiles and ultimately improve recommendation accuracy. Many researches have been done in the area of image classification with e-commerce product images. This paper surveys the e-commerce product image classification techniques. This paper examines current practices, problems, and prospects of product image classification. The emphasis is placed on the account of major advanced classification approaches and also the techniques used for improving classification accuracy. This survey involves reviewing the research work done by different professionals and assembling it into one paper.
Keywords: Classifiers, E-commerce, Image Classification, Machine Learning, Recommendation.
Scope of the Article: Classification