Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/2499788.2499818acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicimcsConference Proceedingsconference-collections
research-article

Personalized image recommendation and retrieval via latent SVM based model

Published: 17 August 2013 Publication History

Abstract

In this paper, we investigate a problem of personalized predicting what images are likely to appear on the Web, given a query word and a database of historical images for multiple users. Inspired by recently emerging interests on personalized image search in information retrieval research, the proposed method can infer users' implicit search intent better and provide more engaging search results according to trends of Web user photos. Firstly, we collect a user historical dataset including 40 users and a panorama recommendation test dataset including 240 pictures, both of which are thoroughly divided into 5 categories, including sky, stone, plant, water, buildings. Second, we develop a predictive framework based on the latent SVM model to retrieve the most relevant images from the dataset at an individual user level, which models the relations between scene-level features and the global-level features that influence it in a globally optimal way. The experimental results on the dataset have validated the effectiveness of the proposed approaches in images recommendation.

References

[1]
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. Computer Vision and Pattern Recognition (CVPR) 2009.
[2]
T. Berg, A. Berg, and J. Shih. Automatic attribute discoveryand characterization from noisy web data. In ECCV, 2010.
[3]
L. Bourdey, S. Maji, T. Brox, and J. Malik. Detecting peopleusing mutually consistent poselet activations. In ECCV,2010.
[4]
P. Felzenszwalb, D. McAllester, and D. Ramanan. Adiscriminatively trained, multiscale, deformable part model. In CVPR, 2008.
[5]
C. Lampert, H. Nickisch, and S. Harmeling. Learning to detectunseen object classes by between-class attribute transfer. In CVPR, 2009
[6]
Y. Wang and G. Mori. A discriminative latent model ofobjectclasses and attributes. In ECCV, 2010.
[7]
C. Shien. L and C. Yung. H Constraint-Optimized Keypoint Inhibition/Insertion Attack:Security Threat to Scale-Space Image Feature Extraction. In ACM MM, 2012.
[8]
X. Yong. W and Z. Qun. Y. Mining In-Class Social Networks for Large-ScalePedagogical Analysis. In ACM MM, 2012.
[9]
S. Roy, T. Mei, W. Jun. Z and S. Peng. L. SocialTransfer: Cross-Domain Transfer Learningfrom Social Streams for Media Applications. In ACM MM, 2012.
[10]
N. Dalal and B. trigs. Histograms of oriented gradients for human detection. In CVPR, 2005.
[11]
A. M. Treisman and G. Gelade, A feature-integration theory of attention, CognitivePsychology, vol. 12, no. 1, pp. 97--136, 1980.
[12]
L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention forrapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254--1259, 1998.
[13]
S. Feng and C. Lang. Beyond tag relevance: integrating visual attention model and multi-instance learning for tag saliency ranking. In ACM CIVR, page 288--295.
[14]
B. Walther, B. Chai, E. Cadddigan, M. Beck and L. Fei. Simple line drawing suffice for functional MRI decoding of natural scene categories. In PNAS, 2011.
[15]
L. Li, H. Su, Y, Lim and L. Fei. Objects as attributes for scene classification. In ECCV, 2010.
[16]
M. Li, Z. Song, Q. Chen and L. Lin. Object-Layout-Aware image retrieval for personal album management. In ECCV, 2012.
[17]
Gunhee Kim and Eric P. Xing Time-Sensitive Web Image Ranking and Retrieval via Dynamic Multi-Task Regression WSDM 2013, Italy, 2013.
[18]
Jinhui Tang, Haojie Li, Guo-Jun Qi, Tat-Seng Chua: Image Annotation by Graph-Based Inference With Integrated Multiple/Single Instance Representations. IEEE TMM,12(2) (2010)
[19]
Jinhui Tang, Shuicheng Yan, Richang Hong, Guo-Jun Qi, Tat-Seng Chua: Inferring semantic concepts from community-contributed images and noisy tags. ACM Multimedia 2009: 223--232
[20]
M. Wang, Y. Sheng, B. Liu, and X.-S. Hua, "In-image accessibility indication," IEEE TMM, vol. 12, no. 4, pp. 330--336, 2010.
[21]
R. Hong, M. Wang, X.-T. Yuan, M. Xu, J. Jiang, S. Yan, and T.-S. Chua, "Video accessibility enhancement for hearing impaired users," ACM TOMCCAP, vol. 7S, no. 1, pp. 24--42, 2011.

Cited By

View all
  • (2019)An event recommendation model using ELM in event-based social networkNeural Computing and Applications10.1007/s00521-019-04344-0Online publication date: 29-Jul-2019

Index Terms

  1. Personalized image recommendation and retrieval via latent SVM based model

      Recommendations

      Comments

      Please enable JavaScript to view thecomments powered by Disqus.

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      ICIMCS '13: Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
      August 2013
      419 pages
      ISBN:9781450322522
      DOI:10.1145/2499788
      • Conference Chair:
      • Tat-Seng Chua,
      • General Chairs:
      • Ke Lu,
      • Tao Mei,
      • Xindong Wu
      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]

      Sponsors

      • NSF of China: National Natural Science Foundation of China
      • University of Sciences & Technology, Hefei: University of Sciences & Technology, Hefei
      • Beijing ACM SIGMM Chapter

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 17 August 2013

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. abstract feature
      2. images recommendation
      3. personalized scene analysis

      Qualifiers

      • Research-article

      Funding Sources

      Conference

      ICIMCS '13
      Sponsor:
      • NSF of China
      • University of Sciences & Technology, Hefei

      Acceptance Rates

      ICIMCS '13 Paper Acceptance Rate 20 of 94 submissions, 21%;
      Overall Acceptance Rate 163 of 456 submissions, 36%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 18 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2019)An event recommendation model using ELM in event-based social networkNeural Computing and Applications10.1007/s00521-019-04344-0Online publication date: 29-Jul-2019

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media