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Assessing product image quality for online shopping

Anjan Goswami, Sung H. Chung, Naren Chittar and Atiq Islam

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Outline

• Motivation• Definition • Features• Experiments• Image quality models.• What’s next?

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Motivation

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Motivation

• Better Aesthetics.• Better customer experience?• Better conversion?• Better branding?• Better looking merchandising pages.

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Applications

• Ranking.• Improve browsing experience.• Guidelines for sellers.• Merchandising.• Many more.

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What is product image quality?• We care mainly about product images.• Product images have specific characteristics.

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Computing image quality

• Machine learning problem.• Factors from images to construct feature

vectors.• Label the data points as one of the classes.• Build a classifier to get the class probabilities.• Alternatively, make a regression model from

human judgment data.

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What factors? How do we compute?

• Size factors such as area, aspect ratio.• Image attributes such as brightness,

saturation, colorfulness, contrast, dynamic range.

• Factors based on background and foreground segmentation.

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BrightnessAverage grayscale intensity of the image.

0.299*red+0.587*green+0.114*blue; 0.18

0.58

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Colorfulness

• Difference between a color against gray. • Many empirical notions.• We use an empirical expression designed in

Natural Color Space. (NCS)• This space has a concept of rg and yb

coordinates.• stdev (rg,yb)+ 0.3 mean (rg,yb)• We compute this globally and for foreground.

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ExampleGlobal FG20 8

60 64

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Saturation

• Many notions and empirical formula.• Average of (max – min) in RGB space. (Used

mainly in photography)

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Example of SaturationScore86

255

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Dynamic range

• Variants of expressions in photography literature and in computer vision.

• We are using a simpler definition used in photography based on range of gray scale intensity.

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Example of Dynamic RangeScore32

100

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Contrast

• Michelson contrast : Range /(max + min) in a color space.

• RMS contrast : stdev of spatial intensities.

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Example of Michelson ContrastScore25

99

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Segmentation

• Grabcut with a heuristic for automated segmentation.

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Segmentation

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Background and foreground area ratio

• Use segmented image.• An approximation is used by using ratio of

pixels in the foreground and in the background.

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ExampleScore12

83

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background and foreground differences.

• Brightness L1 distance.• Contrast L2 distance.

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Properties of background

• stdev of lightness (distance from white in RGB.)

• Mean of lightness. (RGB)

• A score on uniformity of background intensity that approximates texture properties.

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An example

Image Colorfulness

RMS Contrast

Brightness diff

Area ratio

Stdev of lightness of background

Uniformity Whiteness of background

Colorfulness of foreground

38.85

0.30 142.33

0.75 4.22 2.63 100 35.76

44.77

0.086 28.6808

0.66 27.27 16.37 0 39.73

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Data Collection

• Crowdsourcing (52K images)• Professional judgments. (6K images)

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Crowdsourcing

• Has its own challenges.• Require thoughts for framing questions.• Require thoughts for conducting the

experiment.• Cheap labelers can attempt cheating.• Classifier result can be different based on

voting techniques used to find the label.• More judgments are better.

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Image Quality Classes

• Good• Fair• Poor

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Professional Quality Images

• Mostly white, light or uniform background.• Image is free from compression artifact such

as blurring.• Professionally photographed in proper lighting

condition .• Subject has a reasonable size and is in focus.• Example of such images can be seen in

branded retail websites.

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Professional Quality

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Poor images

• Poor or dark background• Can have incomprehensible texture.• Subject small.• Subject unclear.• Bad aspect ratio.• Poor resolution and photography.

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Poor Images

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Fair images

• These are images that are not poor. However, they are not as clean as professional looking photos. (Add examples)

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How do we develop the model

• Multi-class classification with two data sources.

• Direct Regression with crowd-sourced data.• Used Gradient boosted tree.

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Factor importance in Quality Classifier (Classifier)

• Background lightness • Brightness • Aspect ratio • Dynamic range • Background foreground area ratio• Michelson contrast

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Error Rates

• Poor misclassification 10%• Fair misclassification 50% [BAD]• Good misclassification 7%

• However, our training data is so far not perfect.

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Quality Score for Classification

• The quality score is the expected average of the class weights using the class probabilities.

• Currently class weights are simple linear function that maps poor, fair and good to 1,2,3.

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Quality Score201

244

102

119

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Quality Score 177

137

153

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Quality Score122

102

105

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Factor Importance in Regression

• Background lightness• Saturation• Aspect ratio• RMS contrast• Colorfulness

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Comparison of ModelsImage Classifier Regression

98 86

100 57

231 235

133 96

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What’s Next (Semantics)

• Object recognition/relevance.

• Text/watermark detection.

• Human model detection.

>

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Q & A

Thanks!Ideas & questions?