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Multi-class Image Classification Based on Fast Stochastic Gradient Boosting

Informatica (Ljubljana), 2014-06, Vol.38 (2), p.145-145 [Peer Reviewed Journal]

Copyright Slovenian Society Informatika / Slovensko drustvo Informatika Jun 2014 ;ISSN: 0350-5596 ;EISSN: 1854-3871

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  • Title:
    Multi-class Image Classification Based on Fast Stochastic Gradient Boosting
  • Author: Li, Lin ; Wu, Yue ; Ye, Mao
  • Subjects: Accuracy ; Algorithms ; Coding ; Decision trees ; Feature extraction ; Image classification ; Representations ; Stochasticity
  • Is Part Of: Informatica (Ljubljana), 2014-06, Vol.38 (2), p.145-145
  • Description: Nowadays, image classification is one of the hottest and most difficult research domains. It involves two aspects of problem. One is image feature representation and coding, the other is the usage of classifier. For better accuracy and running efficiency of high dimension characteristics circumstance in image classification, this article proposes a novel framework for multi-class image classification based on fast stochastic gradient boosting. They produce the image feature representation by extracting PHOW descriptor of image, then map the descriptor though additive kernels, finally classify image though fast stochastic gradient boosting. The authors propose method of local parallelism and an error control mechanism for simplifying the iterating process. Experiments are tested on two data sets: Optdigits, 15-Scenes. The experiments compare decision tree, random forest, extremely random trees, stochastic gradient boosting and its fast versions. The experiment justifies that stochastic gradient boosting and its extensions are apparent superior to other algorithms on overall accuracy; Their fast stochastic gradient boosting algorithm greatly saves time while keeping high overall accuracy.
  • Publisher: Ljubljana: Slovenian Society Informatika / Slovensko drustvo Informatika
  • Language: English
  • Identifier: ISSN: 0350-5596
    EISSN: 1854-3871
  • Source: Alma/SFX Local Collection
    ProQuest Central
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