Semi-supervised Strategy Research Articles

Help-Training for semi-supervised support vector machines

In this paper, we propose to reinforce the Self-Training strategy in semi-supervised mode by using a generative classifier that may help to train the main discriminative classifier to label the unlabeled data. We call this semi-supervised strategy Help-Training and apply it to training kernel machine classifiers as support vector machines (SVMs) and as least squares support vector machines. In addition, we propose a model selection strategy for semi-supervised training. Experimental results on both artificial and real problems demonstrate that Help-Training outperforms significantly the standard Self-Training. Moreover, compared to other semi-supervised methods developed for SVMs, our Help-Training strategy often gives the lowest error rate.

A Framework of Real Time Hand Gesture Vision Based Human-Computer Interaction

This paper presents a robust framework of human-computer interaction from the hand gesture vision in the presence of realistic and challenging scenarios. To this end, several novel components are proposed. A hybrid approach is first proposed to automatically infer the beginning position of hand gestures of interest via jointly optimizing the regions given by an offline skin model trained from Gaussian mixture models and a specific hand gesture classifier trained from the Adaboost technique. To consistently track the hand in the context of using kernel based tracking, a semi-supervised feature selection strategy is further presented to choose the feature subspaces which appropriately represent the properties of offline hand skin cues and online foreground-background-classification cues. Taking the histogram of oriented gradients as the descriptor to represent hand gestures, a soft-decision approach is finally proposed for recognizing static hand gestures at the locations where severe ambiguity occurs and hidden Markov model based dynamic gestures are employed for interaction. Experiments on various real video sequences show the superior performance of the proposed components. In addition, the whole framework is applicable to real-time applications on general computing platforms.

Kernel Optimization Based Semi-Supervised KBDA Scheme for Image Retrieval

Kernel biased discriminant analysis (KBDA), as a subspace learning algorithm, has been an attractive approach for the relevance feedback in content-based image retrieval. Its performance, however, still suffers from the “small sample learning” problem and “kernel learning” problem. Aiming to solve these problems, in this paper, we present a new semi-supervised scheme of KBDA (S-KBDA), in which the projection learning and the “kernel learning” are interweaved into a constrained optimization framework. Specifically, S-KBDA learns a subspace that preserves both the biased discriminant structure among the labeled samples, and the geometric structure among all training samples. In kernel optimization, we directly optimize the kernel matrix, rather than a kernel function, which makes the kernel learning more flexible and appropriate for the retrieval task. To solve the constrained optimization problem, a fast algorithm based on gradient ascent is developed. The image retrieval experiments are given to show the effectiveness of the S-KBDA scheme in comparison with the original KBDA, and the other two state-of-the-art algorithms.

Semi-supervised pattern classification of medical images: Application to mild cognitive impairment (MCI)

Many progressive disorders are characterized by unclear or transient diagnoses for specific subgroups of patients. Commonly used supervised pattern recognition methodology may not be the most suitable approach to deriving image-based biomarkers in such cases, as it relies on the availability of categorically labeled data (e.g., patients and controls). In this paper, we explore the potential of semi-supervised pattern classification to provide image-based biomarkers in the absence of precise diagnostic information for some individuals. We employ semi-supervised support vector machines (SVM) and apply them to the problem of classifying MR brain images of patients with uncertain diagnoses. We examine patterns in serial scans of ADNI participants with mild cognitive impairment (MCI), and propose that in the absence of sufficient follow-up evaluations of individuals with MCI, semi-supervised strategy is potentially more appropriate than the fully-supervised paradigm employed up to date.

M-MUSICS: an intelligent mobile music retrieval system

Accurate voice humming transcription and efficient indexing and retrieval schemes are essential to a large-scale humming-based audio retrieval system. Although much research has been done to develop such schemes, their performance in terms of precision, recall, and F-measure, among all similarity metrics, are still unsatisfactory. In this paper, we propose a new voice query transcription scheme. It considers the following features: note onset detection using dynamic threshold methods, fundamental frequency (F0) acquisition of each frame, and frequency realignment using K-means. We use a popularity-adaptive indexing structure called frequently accessed index (FAI) based on frequently queried tunes for indexing purposes. In addition, we propose a semi-supervised relevance feedback and query reformulation scheme based on a genetic algorithm to improve retrieval efficiency. In this paper, we extend our efforts to mobile multimedia environments and develop a mobile audio retrieval system. Experiments show our system performs satisfactory in wireless mobile multimedia environments.

Audio-visual human recognition using semi-supervised spectral learning and hidden Markov models

This paper presents a multimodal system for reliable human identity recognition under variant conditions. Our system fuses the recognition of face and speech with a general probabilistic framework. For face recognition, we propose a new spectral learning algorithm, which considers not only the discriminative relations among the training data but also the generative models for each class. Due to the tedious cost of face labeling in practice, our spectral face learning utilizes a semi-supervised strategy. That is, only a small number of labeled faces are used in our training step, and the labels are optimally propagated to other unlabeled training faces. Besides requiring much less labeled data, our algorithm also enables a natural way to explicitly train an outlier model that approximately represents unauthorized faces. To boost the robustness of our system for human recognition under various environments, our face recognition is further complemented by a speaker identification agent. Specifically, this agent models the statistical variations of fixed-phrase speech using speaker-dependent word hidden Markov models. Experiments on benchmark databases validate the effectiveness of our face recognition and speaker identification agents, and demonstrate that the recognition accuracy can be apparently improved by integrating these two independent biometric sources together.

Natural Image Matting with Membership Propagation

We present a semi-supervised technique of object extraction for natural image matting. At first, we present a novel unsupervised graph-spectral algorithm for extraction of homogeneous regions in an image. We next derive a semi-supervised scheme from this unsupervised algorithm. In our method, it is sufficient for users to draw strokes only in one of object and background regions. The semi-supervised optimization problem is solved with an iterative method where memberships are propagated from strokes to their surroundings. We suggest a guideline for placement of strokes by exploiting the same iterative solution process in the unsupervised algorithm. We project the color vectors with the linear discriminant analysis to improve the color discriminability and speed up the convergence of the iterative method. Performance of the proposed method is examined for some images and the results are compared with other methods and ground truth mattes.