Convolutional neural network is powerful for general object recognition. However, its
excellent performance depends largely on huge training set. Facing task like military object …

In the recent days, scene understanding has become hot research topic due to its real usage
at perceiving, analyzing and recognizing different dynamic scenes coverage during GPS …

Recently, support vector machine (SVM) has received much attention due to its good
performance and wide applicability. As a supervised learning algorithm, the standard SVM …

A huge amount of traffic data is archived which can be used in data mining especially
supervised learning. However, it is not being fully used due to lack of accurate accident …

In a supervised setting, the global classification paradigm leverages the whole training data
to produce a single class discriminative model. Alternatively, the local classification …

The low-rank representation (LRR) can get essential row-representation of data and it is
robust to illumination variation, occlusions and other types of noise. This paper presents a …

Logo classification systems have become increasingly important in various industries for
tasks, such as infringement detection and industrial production. However, challenges still …

As an emerging research topic in the field of machine learning, unsupervised domain
adaptation (UDA) aims to transfer prior knowledge from the source domain to help training …

Affective image classification has drawn increasing research attentions in the affective
computing and multimedia communities. Despite many solutions proposed in the literature, it …

In this paper, we combine the random subspace and multi-view together and obtain a novel
approach named semi-supervised multi-random subspace sparse representation (SSM …