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- Title
Multi-class Image Classification Based on Fast Stochastic Gradient Boosting.
- Authors
Lin Li; Yue Wu; Mao Ye
- Abstract
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 paper proposes a novel framework for multi-class image classification based on fast stochastic gradient boosting. We 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. In order to further boost the running efficiency, We 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 (1) stochastic gradient boosting and its extensions are apparent superior to other algorithms on overall accuracy; (2) our fast stochastic gradient boosting algorithm greatly saves time while keeping high overall accuracy.
- Subjects
CLASSIFICATION of pictures; STOCHASTIC processes; BOOSTING algorithms; CODING theory; RANDOM forest algorithms; MACHINE learning; IMAGE databases
- Publication
Informatica (03505596), 2014, Vol 38, Issue 2, p145
- ISSN
0350-5596
- Publication type
Article