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A similarity measure for illustration style

Published: 27 July 2014 Publication History

Abstract

This paper presents a method for measuring the similarity in style between two pieces of vector art, independent of content. Similarity is measured by the differences between four types of features: color, shading, texture, and stroke. Feature weightings are learned from crowdsourced experiments. This perceptual similarity enables style-based search. Using our style-based search feature, we demonstrate an application that allows users to create stylistically-coherent clip art mash-ups.

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  1. A similarity measure for illustration style

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    Published In

    cover image ACM Transactions on Graphics
    ACM Transactions on Graphics  Volume 33, Issue 4
    July 2014
    1366 pages
    ISSN:0730-0301
    EISSN:1557-7368
    DOI:10.1145/2601097
    Issue’s Table of Contents
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 27 July 2014
    Published in TOG Volume 33, Issue 4

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    Author Tags

    1. crowd-sourcing
    2. illustration
    3. learning
    4. style

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