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Fast semantic image retrieval based on random forest

Authors:

Guoping QiuAuthors Info & Claims

MM '12: Proceedings of the 20th ACM international conference on Multimedia

Pages 909 - 912

https://doi.org/10.1145/2393347.2396344

Published: 29 October 2012 Publication History

Abstract

This paper introduces random forest as a computational and data structure paradigm for fusing low-level visual features and high-level semantic concepts for image retrieval. We use visual features to split the tree nodes and use the image labels to supervise the splitting to make images located at the same tree node share similar semantic concepts as well as visual similarities. We exploit such a random forest and define the semantic neighbor set (SNS) of a given image as the union of all images in the leaf nodes that this image falls onto. From SNS we further define the semantic similarity measure (SSM) between two images as the number of trees in which they share the same leaf nodes within a SNS. With SNS and SSM, example-based image retrieval becomes that of first finding the SNS of the querying image and then ranking the images according to the SSMs between the querying image and images in its SNS. We also show that the new technique can be adapted for keyword-based semantic image retrieval. The inherent efficient tree data structure leads to fast solutions. We will present experimental results to show the effectiveness of this new semantic image retrieval technique.

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Cited By

Patel VHiran DDangarwala K(2024)Recent Trends of Information Retrieval System: Review Based on IR Models and ApplicationsProceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications10.1007/978-981-99-9442-7_51(619-629)Online publication date: 23-May-2024
https://doi.org/10.1007/978-981-99-9442-7_51
Bajaj ASharma TSangwan O(2020)Information Retrieval in Conjunction With Deep LearningHandbook of Research on Emerging Trends and Applications of Machine Learning10.4018/978-1-5225-9643-1.ch014(300-311)Online publication date: 2020
https://doi.org/10.4018/978-1-5225-9643-1.ch014
Mukherjee ASil JChowdhury A(2019)A Bag of Constrained Visual Words Model for Image RepresentationProceedings of 3rd International Conference on Computer Vision and Image Processing10.1007/978-981-32-9291-8_32(403-415)Online publication date: 20-Sep-2019
https://doi.org/10.1007/978-981-32-9291-8_32
Show More Cited By

Index Terms

Fast semantic image retrieval based on random forest
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '12: Proceedings of the 20th ACM international conference on Multimedia

October 2012

1584 pages

ISBN:9781450310895

DOI:10.1145/2393347

General Chairs:
Noboru Babaguchi
Osaka University, Japan
,
Kiyoharu Aizawa
The University of Tokyo, Japan
,
John Smith
IBM, USA
,
Program Chairs:
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Thomas Plagemann
University of Oslo, Norway
,
Xian-Sheng Hua
Microsoft, USA
,
Rong Yan
Facebook, USA

Copyright © 2012 ACM.

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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 October 2012

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MM '12

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SIGMM

MM '12: ACM Multimedia Conference

October 29 - November 2, 2012

Nara, Japan

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

Patel VHiran DDangarwala K(2024)Recent Trends of Information Retrieval System: Review Based on IR Models and ApplicationsProceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications10.1007/978-981-99-9442-7_51(619-629)Online publication date: 23-May-2024
https://doi.org/10.1007/978-981-99-9442-7_51
Bajaj ASharma TSangwan O(2020)Information Retrieval in Conjunction With Deep LearningHandbook of Research on Emerging Trends and Applications of Machine Learning10.4018/978-1-5225-9643-1.ch014(300-311)Online publication date: 2020
https://doi.org/10.4018/978-1-5225-9643-1.ch014
Mukherjee ASil JChowdhury A(2019)A Bag of Constrained Visual Words Model for Image RepresentationProceedings of 3rd International Conference on Computer Vision and Image Processing10.1007/978-981-32-9291-8_32(403-415)Online publication date: 20-Sep-2019
https://doi.org/10.1007/978-981-32-9291-8_32
Mukherjee ASil JChowdhury A(2018)Image Retrieval Using Random Forest-Based Semantic Similarity Measures and SURF-Based Visual WordsProceedings of 2nd International Conference on Computer Vision & Image Processing10.1007/978-981-10-7895-8_7(79-90)Online publication date: 12-Apr-2018
https://doi.org/10.1007/978-981-10-7895-8_7
Xu CFushimi SToyoura MXu JMao X(2018)Synthesizing Imagined Faces Based on Relevance FeedbackTransactions on Computational Science XXXII10.1007/978-3-662-56672-5_7(90-105)Online publication date: 7-Mar-2018
https://doi.org/10.1007/978-3-662-56672-5_7
Chien BKu C(2017)Large-scale image annotation with image---text hybrid learning modelsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2221-z21:11(2857-2869)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s00500-016-2221-z
Chien BKu C(2015)A Hybrid Approach for Large-Scale Image ClassificationProceedings of the ASE BigData & SocialInformatics 201510.1145/2818869.2818900(1-6)Online publication date: 7-Oct-2015
https://dl.acm.org/doi/10.1145/2818869.2818900
Rock JGupta TThorsen JGwak JShin DHoiem D(2015)Completing 3D object shape from one depth image2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR.2015.7298863(2484-2493)Online publication date: Jun-2015
https://doi.org/10.1109/CVPR.2015.7298863
Du SZhang WChen SWen Y(2014)Learning Flexible Binary Code for Linear Projection Based Hashing with Random ForestProceedings of the 2014 22nd International Conference on Pattern Recognition10.1109/ICPR.2014.464(2685-2690)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1109/ICPR.2014.464
Fu HZhang QQiu G(2012)Random forest for image annotationProceedings of the 12th European conference on Computer Vision - Volume Part VI10.1007/978-3-642-33783-3_7(86-99)Online publication date: 7-Oct-2012
https://dl.acm.org/doi/10.1007/978-3-642-33783-3_7

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