Semi-supervised image classification in large datasets by using random forest and fuzzy quantification of the salient object

In this paper we are interested in the semi-supervised image classification in large datasets. The main originality of the proposed technique resides in the fuzzy quantification of the salient object in each image in order to guide the semi-supervised learning process during the classification. Indeed, we detect the salient object in each image using soft image abstraction, which allows the subsequent global saliency cues to uniformly highlight entire salient regions. Then, fuzzy quantification was involved for the purpose of improving the correct belonging of pixels to the salient object in each image. For classification, ensemble projection is used, while training a random forest classifier on labeled images with the learned features to classify the unlabeled ones. Experimental results on two challenging large benchmarks show the accuracy and the efficiency of the proposed technique.

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