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In real-word visual applications, distribution mismatch between samples from different domains may significantly degrade classification performance. To improve the generalization capability of classifier across domains, domain adaptation has attracted a lot of interest in computer vision. This work focuses on unsupervised domain adaptation which is still challenging because no labels are available...
Feature selection, selecting the most informative subset of features, is an important research direction in dimension reduction. The combinatorial search in feature selection is essentially a binary optimization problem, known as NP hard, which can be alleviated by learning feature weights. Traditional feature weights algorithms rely on heuristic search path. These approaches neglect the interaction...
This paper presents a novel scheme for Vanishing Points (VPs) estimation and lanes identification through monocular images of mobile robots. VPs detection based on probable vanishing direction hypothesizes and Bayesian posterior probability verification in image Hough space is a foremost contribution. VPs estimation is an optimal resolution based on a weighted objective function. The selected linear...
Hierarchical classification, decomposing the multi-class classification problem into binary ones hierarchically, is efficient when the class quantity getting large. Nowadays, the variety of features to describe data becomes huge and meanwhile the form of these features is diverse, which both make the task of feature fusion crucial for classification. In this paper, an adaptive kernel learning method,...
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