1. 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