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Query modeling for click data based image recognition using graph based propagation and sparse coding

  • Weichen Wu
  • , Min Tan*
  • , Guangjian Zheng
  • , Jun Yu
  • *Corresponding author for this work
  • Hangzhou Dianzi University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We address the fine-grained image recognition problem using user click data, wherein each image is represented as a semantical query-click feature vector. Usually, the query set obtained from search engines is large-scale and redundant. We propose a novel query modeling approach to merge semantically similar queries and construct a compact click feature. We represent each query as a click feature, and design a graph based propagation approach to predict the zero-clicks, ensuring similar images have similar clicks. Afterwards, using this feature, we formulate the problem as a sparse coding based recognition task, wherein the dictionary is discriminatively trained. We evaluate our method for fine-grained image recognition on the public Clickture-Dog dataset. It is shown that, the propagated click feature performs much better than the original one. Also, sparse coding performs better than K-means in query merging.

Original languageEnglish
Title of host publicationInternet Multimedia Computing and Service - 9th International Conference, ICIMCS 2017, Revised Selected Papers
EditorsBenoit Huet, Liqiang Nie, Richang Hong
PublisherSpringer Verlag
Pages191-199
Number of pages9
ISBN (Print)9789811085291
DOIs
StatePublished - 2018
Externally publishedYes
Event9th International Conference on Internet Multimedia Computing and Service, ICIMCS 2017 - Qingdao, China
Duration: 23 Aug 201725 Aug 2017

Publication series

NameCommunications in Computer and Information Science
Volume819
ISSN (Print)1865-0929

Conference

Conference9th International Conference on Internet Multimedia Computing and Service, ICIMCS 2017
Country/TerritoryChina
CityQingdao
Period23/08/1725/08/17

Keywords

  • Click feature
  • Graph based model
  • Image recognition
  • Query modeling
  • Sparse coding

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