5001662
On a Class of Support Vector Kernels Based on Frames in Function Hilbert Spaces
There has been an increasing interest in kernel-based techniques, such as support vector techniques, regularization networks, and gaussian processes. There are inner relationships among those techniques, with the kernel function playing a central role. This article discusses a new class of kernel functions derived from the so-called frames in a function Hilbert space.
2658593
A Probabilistic Framework for SVM Regression and Error Bar Estimation
In this paper, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM) under some convex assumptions for the loss functions. This paper concentrates on the derivation of the evidence and error bar approximation for regression problems. An error bar formula is derived based on the ∈-insensitive loss function.
9676949
Automatic recognition of eye blinking in spontaneously occurring behavior
Previous research in automatic facial expression recognition has been limited to recognition of gross expression categories (e.g., joy or anger) in posed facial behavior under well-controlled conditions (e.g., frontal pose and minimal out-of-plane head motion). We developed a system that detects discrete and important facial actions, (e.g., eye blinking), in spontaneously occurring facial behavior with non-frontal pose, moderate out-of-plane head motion, and occlusion. The system recovers 3D motion parameters, stabilizes facial regions, extracts motion and appearance information, and recognizes discrete facial actions in spontaneous facial behavior. We tested the system in video data from a 2-person interview. Subjects were ethnically diverse, action units occurred during speech, and out-of-plane motion and occlusion from head motion and glasses were common. The video data were originally collected to answer substantive questions in psychology, and represent a substantial challenge to automated AU recognition. In the analysis of 335 single and multiple blinks and non-blinks, the system achieved 98% accuracy.
8809724
Chinese Named Entity Identification Using Class-based Language Model
We consider here the problem of Chinese named entity (NE) identification using statistical language model(LM). In this research, word segmentation and NE identification have been integrated into a unified framework that consists of several class-based language models. We also adopt a hierarchical structure for one of the LMs so that the nested entities in organization names can be identified. The evaluation on a large test set shows consistent improvements. Our experiments further demonstrate the improvement after seamlessly integrating with linguistic heuristic information, cache-based model and NE abbreviation identification.
1704202
Effective steganalysis based on statistical moments of wavelet characteristic function
In this paper, an effective steganalysis based on statistical moments of wavelet characteristic function is proposed. It decomposes the test image using two-level Haar wavelet transform into nine subbands (here the image itself is considered as the LL/sub 0/ subband). For each subband, the characteristic function is calculated. The first and second statistical moments of the characteristic functions from all the subbands are selected to form an 18-dimensional feature vector for steganalysis. The Bayes classifier is utilized in classification. All of the 1096 images from the CorelDraw image database are used in our extensive experimental work. With randomly selected 100 images for training and the remaining 996 images for testing, the proposed steganalysis system can steadily achieve a correct classification rate of 79% for the non-blind Spread Spectrum watermarking algorithm proposed by Cox et ai, 88% for the blind Spread Spectrum watermarking algorithm proposed by Piva et ai, and 91% for a generic LSB embedding method, thus indicating significant advancement in steganalysis.
2659
Toward a unified approach to statistical language modeling for Chinese
This article presents a unified approach to Chinese statistical language modeling (SLM). Applying SLM techniques like trigram language models to Chinese is challenging because (1) there is no standard definition of words in Chinese; (2) word boundaries are not marked by spaces; and (3) there is a dearth of training data. Our unified approach automatically and consistently gathers a high-quality training data set from the Web, creates a high-quality lexicon, segments the training data using this lexicon, and compresses the language model, all by using the maximum likelihood principle, which is consistent with trigram model training. We show that each of the methods leads to improvements over standard SLM, and that the combined method yields the best pinyin conversion result reported.
13951367
Interactive color image segmentation editor driven by active contour model
A new general-purpose color image segmentation editor (CISE) for the purpose of extracting a semantic object is designed, implemented and tested on a number of various natural scene images. Our editor integrates a deformable model and image statistics including intensity, color, gradient and texture. The editor starts with a coarse region segmentation which applies the Canny's operator followed by a low-complexity edge linking algorithm. This segmentation basically builds regions of smooth intensity by closing all dangling edges. Next, a topologically-based region labeling method makes full use of relationship among pixels and produces useful labeled image. Finally, to refine the extracted object of interest (O/sup 2/I), a deformable model based on energy minimization is applied by incorporating both the gradient and region criteria to the external constraint force. These processes are demonstrated through examples on natural scene color images. Experimental results suggest the efficiency and accuracy of the algorithm in its segmentation operations.