Image Correctness for a Product on Marketplace

Abstract

In today\'s digital era, Online marketplaces, which give customers access to a wide range of products, have emerged as the mainstay of retail in the current digital era. The quality and clarity of product photographs have become crucial in guaranteeing customer pleasure and trust as consumers depend more and more on them to make educated purchasing decisions. A ground-breaking solution created to address the issues with erroneous or deceptive product photos is the Image Correctness Application in Product Marketplace. This software uses cutting-edge technology like machine learning and computer vision to validate and improve the quality of product photos that vendors upload.

Introduction

Online marketplaces have become central to modern retail by offering customers easy access to a wide variety of products. High-quality, accurate product images are critical for customer trust and purchase decisions. The Image Correctness Application is proposed as an innovative solution to verify and enhance product images on these platforms, ensuring they truly represent the items being sold.

This system employs advanced computer vision algorithms to detect defects like distortion, color errors, or inconsistencies with product descriptions, combined with Natural Language Processing (NLP) to compare text descriptions with images for accuracy. It also improves image quality through contrast and color correction and tags images using machine learning for better searchability.

The application integrates APIs such as Shopify for data access and leverages crowdsourcing for large-scale human verification. Challenges include managing diverse image sources, scaling to millions of listings, ensuring consistent display across devices, handling multilingual descriptions, and maintaining system adaptability through ongoing learning from user feedback.

The adoption of this technology promises enhanced transparency, customer trust, and increased sales in online marketplaces. Future advancements may include blockchain for image authenticity verification and augmented reality for interactive product visualization. Ethical considerations and human oversight remain important to address bias and complex cases.

Conclusion

The head of this project is focused on the booming e-commerce landscape. In today\'s world, building a trustworthy relationship between sellers and buyers is crucial. The images we see on websites should match what customers receive, and that\'s where this app comes in. High-quality images and their proper sizing play a vital role in our marketplace. We\'re ensuring top-notch image quality by using PSNR technology. Image handling is managed through a CNN algorithm, while color correction is achieved with the LAB algorithm. Ultimately, we aim to create a website that is affordable, cost-effective, and realistic. Investing in advanced image recognition technologies can streamline the process of verifying image accuracy. Machine learning algorithms can be trained to spot inconsistencies between product images and their descriptions, flagging any potentially misleading or inaccurate listings for further review.

References

[1] Ali, A. and Sharma, S. (2017). Content based image retrieval using feature extraction with machine learning, 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 1048– 1053. [2] Boriya, A., Malla, S. S., Manjunath, R., Velicheti, V. and Eirinaki, M. (2019). Viser: A visual search engine for eretail, 2019 First International Conference on Transdisciplinary AI (TransAI), pp. 76–83. [3] Dagan, A., Guy, I. and Novgorodov, S. (2021). An image is worth a thousand terms? analysis of visual ecommerce search, Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’21, Association for Computing Machinery, New York, NY, USA, p. 102–112. [4] Diyasa, I. G. S. M., Alhajir, A. D., Hakim, A. M. and Rohman, M. F. (2020). Reverse image search analysis based on pre-trained convolutional neural network model, 2020 6th Information Technology International Seminar (ITIS), pp. 1–6. [5] Gang Zhou, W., Li, H. and Tian, Q. (2017). Recent advance in content-based image retrieval: A literature survey, ArXiv abs/1706.06064.

Copyright

Copyright © 2025 Aditi Gupta, V. A. Mayavathi, V. Hemavathi, Ballani Vignesh, K. Gnanendra Varma, Dr. Galiveeti Poornima. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.