Application of CBIR in E-commerce
Shweta Sadwani1, Vaibhavi Sangawar2, Rushabh Sanap3, Akanksha Kakade3, Minakshi Vharkate4
1Shweta Sadwani, B. Tech. in Computer Science, MIT Academy of Engineering, Pune, India.
2Vaibhavi Sangawar, B. Tech. in Computer Science, MIT Academy of Engineering, Pune, India.
3Rushabh Sanap, B. Tech. in Computer Science, MIT Academy of Engineering, Pune, India.
4Akanksha kakade, B. Tech. in Computer Science, MIT Academy of Engineering, Pune, India.
5Minakshi N.Vharkate, Sr. Assistant Professor, MIT Academy of Engineering, Pune, India.
Manuscript received on June 30, 2020. | Revised Manuscript received on July 27, 2020. | Manuscript published on July 30, 2020. | PP: 439-444 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3587079220/2020©BEIESP | DOI: 10.35940/ijrte.B3587.079220
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© 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 rise of technology and the rapidly increasing inventions in Science have completely changed many aspects of the world today. Many sectors such as communication, banking, media, etc. have gained momentum because of the internet. Online shopping is one such sector that has flourished in recent times because of the internet. This paper presents a method which employs the system of Content Based Image Retrieval (CBIR) in online shopping. Using this system, the time required to shop online will be reduced. CBIR is the activity of fetching images from the database which have some similarity to the given query image. Traditionally customers would have to search from different categories and apply various filters to buy the product that they want. But in this system, they will be provided with an option to directly upload the image of the product that they wish to buy. If similar products are available, it will be displayed to the customer immediately. Thus, the time required for a customer to buy a product reduces considerably thereby making the shopping experience fun, easy and convenient. The system works in a way such that when an image is uploaded, the features of this image are extracted by using the deep learning method of Convolutional Neural Network (CNN). These extracted features are compared with the features of the available images stored in the database. Then, the similarity measure is calculated and images that are akin to the query image are found and are set out as result. This method significantly helps in reducing the time required to search for a particular product.
Keywords: Classification and Indexing, Content based Image Retrieval, Convolutional Neural Network, deep learning, image processing .