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Sketch-based Image Retrieval via Shape Words

Authors:

Changcheng Xiao,

Changhu Wang,

Liqing Zhang,

Lei ZhangAuthors Info & Claims

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

Pages 571 - 574

https://doi.org/10.1145/2671188.2749360

Published: 22 June 2015 Publication History

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Abstract

The explosive growth of touch screens has provided a good platform for sketch-based image retrieval. However, most previous works focused on low level descriptors of shapes and sketches. In this paper, we try to step forward and propose to leverage shape words descriptor for sketch-based image retrieval. First, the shape words are defined and an efficient algorithm is designed for shape words extraction. Then we generalize the classic Chamfer Matching algorithm to address the shape words matching problem. Finally, a novel inverted index structure is proposed to make shape words representation scalable to large scale image databases. Experimental results show that our method achieves competitive accuracy but requires much less memory, e.g., less than 3% of memory storage of MindFinder. Due to its competitive accuracy and low memory cost, our method can scale up to much larger database.

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Y. Cao, C. Wang, L. Zhang, and L. Zhang. Edgel index for large-scale sketch-based image search. CVPR, 2011.

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Y. Cao, H. Wang, C. Wang, Z. Li, L. Zhang, and L. Zhang. Mindfinder: Interactive sketch-based image search on millions of images. MM, 2010.

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Cited By

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Xu PHospedales TYin QSong YXiang TWang L(2023)Deep Learning for Free-Hand Sketch: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.314885345:1(285-312)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TPAMI.2022.3148853
Chen YNi BChen XHu Z(2023)Editable Image Geometric Abstraction via Neural Primitive Assembly2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.02149(23457-23466)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.02149
Birari DHiran DNarawade V(2022)Image Retrieval through sketches based on Descriptor with Data Retrieval using Reversibility Method2022 International Conference for Advancement in Technology (ICONAT)10.1109/ICONAT53423.2022.9726045(1-6)Online publication date: 21-Jan-2022
https://doi.org/10.1109/ICONAT53423.2022.9726045
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Index Terms

Sketch-based Image Retrieval via Shape Words
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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Information

Published In

ICMR '15: Proceedings of the 5th ACM on International Conference on Multimedia Retrieval

June 2015

700 pages

ISBN:9781450332743

DOI:10.1145/2671188

General Chairs:
Alex Hauptmann
Carnegie Mellon University, USA
,
Chong-Wah Ngo
City University of Hong Kong, China
,
Xiangyang Xue
Fudan University, China
,
Program Chairs:
Yu-Gang Jiang
Fudan University, China
,
Cees Snoek
University of Amsterdam and Qualcomm Research Netherlands
,
Nuno Vasconcelos
University of California, San Diego, USA

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 June 2015

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Funding Sources

National Key Basic Research Program of China
National Natural Science Foundation of China

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ICMR '15

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SIGMM

ICMR '15: International Conference on Multimedia Retrieval

June 23 - 26, 2015

Shanghai, China

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ICMR '15 Paper Acceptance Rate 48 of 127 submissions, 38%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Xu PHospedales TYin QSong YXiang TWang L(2023)Deep Learning for Free-Hand Sketch: A SurveyIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.314885345:1(285-312)Online publication date: 1-Jan-2023
https://doi.org/10.1109/TPAMI.2022.3148853
Chen YNi BChen XHu Z(2023)Editable Image Geometric Abstraction via Neural Primitive Assembly2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.02149(23457-23466)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.02149
Birari DHiran DNarawade V(2022)Image Retrieval through sketches based on Descriptor with Data Retrieval using Reversibility Method2022 International Conference for Advancement in Technology (ICONAT)10.1109/ICONAT53423.2022.9726045(1-6)Online publication date: 21-Jan-2022
https://doi.org/10.1109/ICONAT53423.2022.9726045
Alaniz SMancini MDutta AMarcos DAkata Z(2022)Abstracting Sketches Through Simple PrimitivesComputer Vision – ECCV 202210.1007/978-3-031-19818-2_23(396-412)Online publication date: 22-Oct-2022
https://doi.org/10.1007/978-3-031-19818-2_23
Chan SCheung Y(2021)How to Make a Query in Image Retrieval with Partial Information Extracted from Multiple Image Samples?International Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142154021535:07(2154021)Online publication date: 27-Mar-2021
https://doi.org/10.1142/S0218001421540215
Tang MFalomir ZSheng Y(2020)A Multilevel Road Alignment Model for Spatial-Query-by-SketchApplied Sciences10.3390/app1021768510:21(7685)Online publication date: 30-Oct-2020
https://doi.org/10.3390/app10217685
Song GTan X(2020)Deep code operation network for multi-label image retrievalComputer Vision and Image Understanding10.1016/j.cviu.2020.102916193:COnline publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1016/j.cviu.2020.102916
Imani SAlaee SKeogh E(2019)Putting the Human in the Time Series Analytics LoopCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3317308(635-644)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3317308
Tang XWei HHao K(2019)Using a Vertical-Stream Variational Auto-Encoder to Generate Segment-Based Images and Its Biological Plausibility for Modelling the Visual PathwaysIEEE Access10.1109/ACCESS.2018.28850067(99-110)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2885006
Birari DHiran DNarawade V(2019)Survey on Sketch Based Image and Data RetrievalICCCE 201910.1007/978-981-13-8715-9_34(285-290)Online publication date: 2-Aug-2019
https://doi.org/10.1007/978-981-13-8715-9_34
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IdeaPanel: A Large Scale Interactive Sketch-based Image Search System

Sketch-based Image Retrieval using Generative Adversarial Networks

Sketch-based image retrieval using keyshapes

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