Open AccessSnapshop-An Image Based Search Application
Author(s) -
Karan D. Argade,
Dhanashree M. Gaware,
Prajkta S. Umap,
Savita P. Nalawade,
Snehal Baravkar
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/ijsrst2183151
Subject(s) - computer science , product (mathematics) , purchasing , sophistication , clothing , plan (archaeology) , matching (statistics) , e commerce , information retrieval , world wide web , advertising , data science , business , marketing , social science , statistics , geometry , mathematics , archaeology , sociology , history
E-commerce has been growing rapidly over the past few years, Peoples uses them to buy and sell products. Since In these offline stores, they face many problems such as inability. For many online customers, image recognition of clothing and to identify the style, the color, and, in fact, it is a challenge to the sophistication of the fashion industry. In e-commerce, the online platform primarily offers text-based search capabilities. They can search many product searches, but they cannot manage searches based on product features, for example, colors or t-shirt patterns. Often, it is difficult for the user to make this determination features when searching for a product. Furthermore, an increasing number of consumers are depending on social media to make purchasing decisions. Consumers are trying to discover what is going on right now and are looking for the same things. This brings us to the need for a visual commerce platform, or a plan, which recommends products based on users, provided that the product images. The database uses a flexible neural network. You extract data using this deep neural network of image recognition, pattern matching and are very effective in testing fabric prediction.