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Project Pipeline and outlines SVM (Support Vector Machine)
Professor: Jan.P.Allebach Graduate Mentor: Zhi Li Graduate Mentor Ni Yan Photos Aesthetic Score Calculator (Imaging and Printing) Department of Electrical and Computer Engineering Poshmark Partners: Gautam Gowala Sathya Sundaram Project Pipeline and outlines Background Poshmark Network Framework Other Poshmarkers: 1 Poshmark is an online clothing retailing shop where people can buy and sell clothes and other accessories. We have developed a program that determines the categories that the item belongs to so, that the customer doesn’t have to fill it up manually. It also scores the photos and gives users tips to get a better and more appealing photo of the stuff that they want to sell. Shuheng Lin Aesthetic Score calculator Kartikeya Mishra What we do? Deep Learning Category Recognition Image Processing Image Clustering using K-Means algorithm Image segmentation to find connected components Image Fingerprinting Develop an algorithm to detect duplicate images Result : True (which means the images are duplicates) White Balancing Bags Deep Learning Deep Learning Category recognition Fine-tuned a neural network to classify images into 16 categories Roro Okpu Franklin Shiao Results Original K= 2 K= 8 Apoorv Naidu Ankulam Nikki Beesetti Future plans SVM (Support Vector Machine) Photo Beautification Advisor : Photo recommendation on photo beatification based on color , once we have a database with color data . Photo Duplications: Improve our algorithm so it can detect even cropped images as duplicates. Deep Learning: Keep working on our SVM predictor so that we can eliminate false results such as the one where a pair of jeans was detected as pants. Photo Quality Feature Extraction Collected ground truth to have a huge database to test our SVM predictor Currently working with white & non-white background and modelled- unmodeled pictures Language Used: MATLAB Original Result
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