Exploring Representativeness and Informativeness for Active Learning

Abstract

How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without any assumption on data? This paper proposes a general active learning framework that effectively fuses the two criteria. Inspired by a two-sample discrepancy problem, triple measures are elaborately designed to guarantee that the query samples not only possess the representativeness of the unlabeled data but also reveal the diversity of the labeled data. Any appropriate similarity measure can be employed to construct the triple measures. Meanwhile, an uncertain measure is leveraged to generate the informativeness criterion, which can be carried out in different ways. Rooted in this framework, a practical active learning algorithm is proposed, which exploits a radial basis function together with the estimated probabilities to construct the triple measures and a modified best-versus-second-best strategy to construct the uncertain measure, respectively. Experimental results on benchmark datasets demonstrate that our algorithm consistently achieves superior performance over the state-of-the-art active learning algorithms.

Visual Tracking Under Motion Blur

“…The observation is that the opening/closing operation of the shutter happens instantaneously, hence there is no Gaussian temporal blur. In [17], motivated by the success of sparse representation applied to vision tasks [20]- [24], [43], [47], [49], a unified sparse approximation framework is presented for integrating the visual tracking with the motion blur problem. The dictionary for sparse representation contains normal templates, blur templates and trivial templates.…”

Background Information Self‐Learning Based Hyperspectral Target Detection

“…It is assumed that the not interested region is homogeneous and can be represented by a multivariate normal distribution, and, by using the sample data, we could estimate the background covariance matrix if the background information and a priori target information are known [17]. Kelly proposed the generalized likelihood ratio structure detection algorithm which is based on unstructured background firstly, and we obtain adaptive cosine consistency evaluation ACE and adaptive matched filter AMF on this basis [18]. These methods are calculated for the background covariance estimates directly from the entire image data, but this will cause some error due to the absence of the object information to be extracted.…”

A novel grouped sparse representation for face recognition

“…However, face recognition still faces a lot of challenges such as the various lightings, facial expressions, poses and environments [10,13,14,33,35,42,54,57,65,68]. In order to overcome these challenges, a lot of representation-based classification methods (RBCMs) [15,29,31,37,52,53,63,64,68] are proposed such as SRC [52], collaborative representation classification (CRC) [63], two-phase test sample representation (TPTSR) [53], linear regression classification (LRC) [37], feature space representation method [61], an improvement to the nearest neighbor (INNC) classification [55], etc. SRC tries to represent the test sample by an optimal linear combination of the training samples.…”