POSTER TEMPLATE BY: Background Objectives Psychophysical Experiment Photo OCR Design Project Pipeline and outlines ❑ Deep Learning  Brand Recognition: Photo OCR ❑ Image Processing  Color Recognition: K-Means Algorithm  Background Removing ❑ Color Naming  Collect and Categorize Colors from Online Retailers RGB Color Space: Based on tristimulus theory of human vision R: red G: green B: blue LCH Color Space The Lch color model is very useful for retouching images in a color managed workflow, using high-end editing applications. Lch is device-independent. L: lightness C: Chroma H: Hue  Downloading Pictures from 20 On-line Retailers  Asos, Nordstrom, JCPenny, Walmart…  Categorize according to different Colors  Most Popular Colors: Black, Red Pink, White… Conclusion Background Color Extraction Systematic Study of Color Categories Online Fashion Shopping Photos Aesthetic Quality Assessment Department of Electrical and Computer Engineering Professor: Jan.P.Allebach Student: Yin Wang Student: Tenglun Tan Poshmark is a mobile and online marketplace for women's fashion based in Menlo Park, California. When customer are trying to sell things on Website, they have to fill out a long list including Category, Size, Brand, Color, New with tags, Original Price. We are trying to train a system that can detect the Category, Color and Brand using Image Processing and Deep Learning. Color Recognition Design Gold, Red  Photo OCR Pipeline:  Color Naming Pipeline :  Color Recognition: K-Means Algorithm K-Means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analyzing data mining. Use Python2.7 and OpenCV3.0 to extract color from photo using K-Means Application: Color Recognition of an item in the photo. Demo: We first use Yin’s tool to remove the background, then apply Tenglun’s K-Means Algorithm to get the histogram of the distribution of color of the item in the photo.  Software: Python 2.7 and OpenCV 3.0.  Deep Learning Method: Convolutional Neural Network(CNN). Brown, beige, dark red Red, dark green, beige  Step1: Text detection Label the training set with positive examples and negative examples. Positive examples (y=1)Negative examples (y=0) Then we use the sliding window detection to find the location of the text. Expansion  Step 2: Character Segmentation:  Step 3: Character Recognition: : Add background to different font Add distortion, noise  Application: Artificial Intelligence.  When customer upload an item to sell, he/she will get the information of Brand Recognition(using our deep learning approach) and Color Recognition(using our image processing approach).  Background Removing: Since the pattern is already extracted from the photo, we can ask the customer whether they want to remove the background to improve the quality of photo.  Future Plan: Apply Ceiling Analysis to figure out what part of Pipeline to work on Next.  Ceiling Analysis: Estimating the errors due to each component.  Matlab Approach: Read photos from the designated path in Matlab Approximate the range of RGB value of the item you want to extract Input desired range of RGE value Set other pixels as pure white (256,256,256) Output would be the item after extracted  Result:  OpenCV Approach (C++ source code) Read photos from the designated path Input different layers on the photos you wan to extract (usually 4 or 5) Do not need to use color space to define to extract the item The main item you want would be kept in the photo  Result: Original photoAfter MATLAB extraction Original photo After defining layers (4 layers) After extraction Graduate Mentor: Zhi Li Poshmark Partners: Gautam Gowala Sathya Sundaram