1. POSTER TEMPLATE BY: www.PosterPresentations.com Background Objectives Psychophysical Experiment Smoothness Features Project Pipeline and outlines The purpose of this poster presentation is to provide a brief overview of progress to create an onlnie fashion shopping photos aesthetic quality assessment. Our goal is to develop a method to automatically generate aesthetic quality scores for photos. Focus on customer-uploaded fashion item photos on customer-to-customer (C2C) fashion shopping website. Mostly taken by amateur photographers. When customers uploaded item photos, we can give a feedback on the aesthetic quality. If the quality is not satisfactory, we may suggest customers retaking photos. Conclusion Lightness and Color Features Photo Feature Design We use 26 photo aesthetic quality features. They are divided into 4 groups based on their characteristics and influence on photo aesthetic quality. Lightness and Color Features Subject-based Features Smoothness Features Low Depth of Field Features Online Fashion Shopping Photos Aesthetic Quality Assessment Department of Electrical and Computer Engineering Professor: Jan.P.Allebach Mentor: Jianyu Wang Student: Tenglun Tan Poshmark is a mobile and online marketplace for women's fashion based in Menlo Park, California. Available on iOS and Android, Poshmark provides women a platform to sell their clothing and accessories, purchase items from other users and attend Posh Parties, an in-app and offline feature coined by the company to describe real- time shopping events centered on a specific trend or set of brands. According to its 2013 annual report, over 1.5 million items have been sold in its mobile fashion marketplace with Coach, Tory Burch and J. Crew as some of its top- selling brands. Build dataset and collect ground truth from psychophysical experiment. Photo feature design. Analysis of relevance between features and photo aesthetic quality. Construct optimal feature subset for predictor training. Prediction accuracy analysis. Training Photo Set Psychopyhsical Experiment Photo Quality Feature Calculation Photo Quality Feature Calculation SVM Training SVM Model New Photo SVM Prediction Aesthetic Score We collected a database of 500 photos from our sponsor (www.poshmark.com).www.poshmark.com Built a GUI and ask experiment participants to input the aesthetic quality score for each photo. The rating is based on a 1 to 10-point scale, where 1 denotes worst quality and 10 denotes best quality. Low-level lightness and color features: Average lightness (f1), image colorfulness (f2), contrast (f3), average saturation (f4), average hue (f5), and hue count (f6) High-level: color harmony Analyze the distribution of pixels hues related to 7 color harmony templates (each template can be rotated at any angel). Calculate the sum of arc length distances between image pixels and nearest color template border to select the fitting template: Propose modification: Subject-based Features An aesthetically pleasing photo should clearly emphasize the subject. Approach: adopt a saliency algorithm to extract photo subject then calculate features based on the result. Saliency algorithm: the Saliency Filters algorithm. Thresholding algorithm: valley-emphasis thresholding. Compute subject-based features with the saliency mask: Number of salient regions (f8), aggregate area size of all salient regions (f9), subject-background lightness difference (f10), subject-background hue difference (f11), subject-background saturation difference (f12), and modified rule of thirds (f13). The smoothness of a photo can tell if it is out of focus or noisy. Use Laplacian and wavelet decomposition to compute smoothness. First build a 3-level decomposition pyramid. Then calculate Wavelet power summation (f14, f15, f16) and Laplacian power summation (f17, f18, f19). Original photo 3-level Laplacian pyramid A blurred background is a significant characteristic of high aesthetic quality photos that contain dominant subjects. Also calculated with Wavelet and Laplacian decomposition. Adopt Center detail strength ratio (f21, f22 ) feature and the edge energy bounding box (f23, f24). We propose another feature: sum of weighted distance (f25, f26). A small value of sum of weighted distance indicates that the details of the photo concentrate around the center of mass, and therefore means that the background is blurred. Developed a procedure to automatically predict the aesthetic quality of fashion item photos. Designed new features to measure different aspects of photo aesthetic quality. Analyzed the relevance between multiple photo aesthetic quality features and actual photo aesthetic quality. Adopted wrapper feature selection method to select the optimal feature subset for predictor training. Achieved satisfactory prediction accuracy in cross- validation.