Nowadays, image classification is one of the hottest and most difficult research domains. It involves twoaspects of problem. One is image feature representation and coding, the other is the usage of classifier. Forbetter 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 stochasticgradient 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 gradientboosting. In order to further boost the running efficiency, We propose method of local parallelism and anerror 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 gradientboosting and its extensions are apparent superior to other algorithms on overall accuracy; (2) our faststochastic gradient boosting algorithm greatly saves time while keeping high overall accuracy.
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