palette power: enabling visual search through colors

Palette Power: Enabling Visual Search through Colors

Aug 14, 2013

eBay Research Labs

http://labs.ebay.com

Changing Landscape of Search

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Visual Search for Fashion

eBay

Inventory

Given an item image,

find similar eBay inventory

Query Image

Similar Items

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Item Similarity

Fre

quency

Color Distributions

Dots Floral Checks

Patterns & Textures

Styles

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Approach Overview

Large Image Data Speed Requirements

Take Advantage of Context

Our Approach – The Power of Color Distributions

Color Spaces

𝑑 = 𝑓(𝑖1, 𝑖2) Distance Functions

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Challenges (1/3)

Low contrast between background and foreground

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Challenges (2/3)

Background Clutter

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Challenges (3/3)

Lighting Variation

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Insights From Data

Object localization using spatial priors

Choosing the right color space

Why Object Localization?

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Cluttered background degrades performance.

State-of-the-art segmentation too expensive.

Need a fast and reliable solution!

Spatial Prior to the rescue!

Understanding Spatial Prior

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Choosing Best Color Space

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Handling Color Confusion

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Generating Color Histogram

Faster Lookup via k-center

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Scaling via backend clustering/indexing.

Potential for semantic/intent diversification - e.g. query t-shirt image where you like style but not colors

Achieves 60x speedup close to 70% overlap!

Median speed-up Median %-overlap

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Architecture

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Experiment I – Fashion Dataset

Categories: Women’s Dresses, Tops & Blouses, Coats & Jackets,

Skirts, Sweaters and T-Shirts

Data Sets: 1600 Queries & 1 Million Inventory images, 15 users for

30 days

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Results - Solid Queries

Results - Pattern Queries

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Experiment II – Generic ecommerce Dataset

Categories: Toys, Sports, Camera

Data Sets: Query & Inventory sets for each category

Ground Truth: ~15 per query

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Example Inventory Images

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Toys

Sports

Camera

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MAP Performance

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Experiment III – INRIA Holidays Dataset

Categories: Personal Holidays Photos

Data Sets: 500 Queries (1 per group) & 1491 Inventory Images

Ground Truth: Human Annotations

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MAP Performance

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Computational Costs

Feature Extraction Time 10 ms

Retrieval Time 80 ms

Feature Vector Size 196 Bytes

Memory Required 190 MB

Machine Stats: 24 GB RAM, 2.53GHz

Index Size: 1M+

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Summary

Color a fundamental cue

Spatial Prior can eliminate need for expensive

background removal

Future work to focus on efficient descriptors

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Questions?