Robust speech recognition method based on discriminative environment feature extraction

It is an effective approach to learn the influence of environmental parameters,such as additive noise and channel distortions,from training data for robust speech recognition.Most of the previous methods are based on maximum likelihood estimation criterion.However,these methods do not lead to a minimum error rate result.In this paper,a novel discriinative learning method of environmental parameters,which is based on Minimum Classification Error (MCE) criterion,is proposed.In the method,a simple classifier and the Generalized Probabilistic Descent (GPD)algorithm are adopted to iteratively learn the environmental parameters.Consequently,the clean speech features are estimated from the noisy speech features with the estimated environmental parameters,and then the estimations of clean speech features are utilized in the back-end HMM classifier,Experiments show that the best error rate reudction of 32.1% is obtained,tested on a task of 18 isolated confusion Korean words,relative to a conventional HMM system.     

Mesh segmentation using feature point and core extraction

Mesh segmentation has become a necessary ingredient in many applications in computer graphics. This paper proposes a novel hierarchical mesh segmentation algorithm, which is based on new methods for prominent feature point and core extraction. The algorithm has several benefits. First, it is invariant both to the pose of the model and to different proportions between the model’s components. Second, it produces correct hierarchical segmentations of meshes, both in the coarse levels of the hierarchy and in the fine levels, where tiny segments are extracted. Finally, the boundaries between the segments go along the natural seams of the models.     

基于多尺度局部特征的图像分割模型

为了解决图像分割中灰度不均匀和初始轮廓敏感的问题,提出一种基于多尺度局部特征的图像分割模型.与传统局部邻域定义在方形区域不同,该模型采用圆形区域来获取更多的局部信息;考虑到局部区域灰度的变化程度不一,提出利用多尺度结构与均值滤波器相结合的方法获得多尺度局部灰度信息;通过转换灰度不均匀模型得到一个逼近真实信息的图像,并将其融合进局部高斯分布拟合(LGDF)模型,构造出基于多尺度局部特征的能量泛函.从理论分析和实验结果表明:由于多尺度结构弱化了灰度不均匀的影响,该模型既能快速、准确地分割灰度不均匀图像,又表现出对初始轮廓具有较强的鲁棒性.     

海洋SAR图像分割及边缘梯度特征的提取

为提高合成孔径雷达(SAR)海洋溢油图像的分割效果,得到海洋表面溢油的准确信息,提出一种改进的迭代算法,能更为有效地选取阈值,尤其适用于暗斑区域与背景灰度值差异悬殊的情况.对海洋SAR图像进行预处理,并且对处理后的SAR样本图像进行边缘分割,在此过程中通过优化迭代算法得到一种新的选取阈值的方法,运用此方法成功将暗斑区域与海洋背景分离;使用5*5窗口提取样本图像的边缘梯度特征量,对梯度均值与方差进行分析、理解达到识别SAR图像中的溢油区域的目的.将仿真结果与传统分割结果进行了比较,比较结果表明了该方法在选取阈值进行图像分割方面要优于其它算法.     

Image relaxation: restoration and feature extraction

The techniques of a posteriori image restoration and iterative image feature extraction are described and compared. Image feature extraction methods known as graduated nonconvexity (GNC); variable conductance diffusion (VCD), anisotropic diffusion, and biased anisotropic diffusion (BAD), which extract edges from noisy images, are compared with a restoration/feature extraction method known as mean field annealing (MFA). All are shown to be performing the same basic operation: image relaxation. This equivalence shows the relationship between energy minimization methods and spatial analysis methods and between their respective parameters of temperature and scale. As a result of the equivalence, VCD is demonstrated to minimize a cost function, and that cost is specified explicitly. Furthermore, operations over scale space are shown to be a method of avoiding local minima     

Video sequence feature extraction and segmentation using likelihood regression model

The development of digital technology is utilized by people to capture and share video frames. At present, rather than capturing images, people are interested in recording video footage for exploring information. Here, retrieval of video from large databases is challenging due to the continuous frame count. To overcome these challenges associated with the retrieval of video from available databases, this research proposed a likelihood-based regression approach for video processing. To improve the retrieval accuracy of video sequences, the proposed method utilizes a likelihood estimation technique integrated with a regression model. The likelihood estimate measures the pixel level roughly for estimating the pixel range, after which the regression approach measures the pixel level for transforming certainly blurred and unwanted pixels. In the proposed likelihood regression approach, the video is converted into a video frame and stored in a database. Query frames are taken into account by the generated database depending on the features which are used for a given video to be retrieved. The significant video retrieval performance obtained from the simulation results for the proposed likelihood-based regression model shows that the proposed model performs well over the other state-of-the-art techniques.

     

Exploiting Voronoi diagram properties in face segmentation and feature extraction

Segmentation of human faces from still images is a research field of rapidly increasing interest. Although the field encounters several challenges, this paper seeks to present a novel face segmentation and facial feature extraction algorithm for gray intensity images (each containing a single face object). Face location and extraction must first be performed to obtain the approximate, if not exact, representation of a given face in an image. The proposed approach is based on the Voronoi diagram (VD), a well-known technique in computational geometry, which generates clusters of intensity values using information from the vertices of the external boundary of Delaunay triangulation (DT). In this way, it is possible to produce segmented image regions. A greedy search algorithm looks for a particular face candidate by focusing its action in elliptical-like regions. VD is presently employed in many fields, but researchers primarily focus on its use in skeletonization and for generating Euclidean distances; this work exploits the triangulations (i.e., Delaunay) generated by the VD for use in this field. A distance transformation is applied to segment face features. We used the BioID face database to test our algorithm. We obtained promising results: 95.14% of faces were correctly segmented; 90.2% of eyes were detected and a 98.03% detection rate was obtained for mouth and nose.     

Image super-resolution using supervised multi-scale feature extraction network

Multimedia Tools and Applications - Image super-resolution using deep convolutional networks have recently achieved great successes. However, previous studies have failed to consider the spatial...     

Skin feature extraction and processing model for statistical skin age estimation

The convergence of information and medical technologies has resulted in the emergence and active development of the ubiquitous healthcare (U-Healthcare) industry. The U-healthcare industry provides telepathology and anytime-anywhere wellness services. The main purpose of these wellness services is to provide health information to improve the quality of life. Human skin is an organ that can be easily examined without expensive devices. In addition, there has recently been rapidly increasing interest in skin care products, resulting in a concomitant increase in their consumption. In this paper, we propose a new scheme for a self-diagnostic application that can estimate the actual age of the skin on the basis of the features on a skin image. In accordance with dermatologists’ suggestions, we examined the length, width, depth, and other cell features of skin wrinkles to evaluate skin age. Using our highly developed image processing method, we could glean detailed information from the surface of the skin. Our scheme uses the extracted information as features to train a support vector machine (SVM) and evaluates the age of a subject’s skin. Evaluation of our proposed scheme showed that it was more than 90% accurate in the analysis of the skin age of three different parts of the body: the face, neck, and hands. Therefore, we believe our model can be used as a standard or as a scale to measure the degree of damage or the aging process of the skin. This scheme is implemented into our Self-Diagnostic Total Skin Care system, and the information obtained from this system can be utilized in various areas of medicine.     

Fuzzy-based discriminative feature representation for children's speech recognition

Automatic recognition of the speech of children is a challenging topic in computer-based speech recognition systems. Conventional feature extraction method namely Mel-frequency cepstral coefficient (MFCC) is not efficient for children's speech recognition. This paper proposes a novel fuzzy-based discriminative feature representation to address the recognition of Malay vowels uttered by children. Considering the age-dependent variational acoustical speech parameters, performance of the automatic speech recognition (ASR) systems degrades in recognition of children's speech. To solve this problem, this study addresses representation of relevant and discriminative features for children's speech recognition. The addressed methods include extraction of MFCC with narrower filter bank followed by a fuzzy-based feature selection method. The proposed feature selection provides relevant, discriminative, and complementary features. For this purpose, conflicting objective functions for measuring the goodness of the features have to be fulfilled. To this end, fuzzy formulation of the problem and fuzzy aggregation of the objectives are used to address uncertainties involved with the problem.The proposed method can diminish the dimensionality without compromising the speech recognition rate. To assess the capability of the proposed method, the study analyzed six Malay vowels from the recording of 360 children, ages 7 to 12. Upon extracting the features, two well-known classification methods, namely, MLP and HMM, were employed for the speech recognition task. Optimal parameter adjustment was performed for each classifier to adapt them for the experiments. The experiments were conducted based on a speaker-independent manner. The proposed method performed better than the conventional MFCC and a number of conventional feature selection methods in the children speech recognition task. The fuzzy-based feature selection allowed the flexible selection of the MFCCs with the best discriminative ability to enhance the difference between the vowel classes.     

Breast density analysis based on glandular tissue segmentation and mixed feature extraction

Multimedia Tools and Applications - Breast cancer poses a threat to the lives of many women. Breast density is a closely related indicator of breast cancer risk. The aim of this paper is to propose...     

Locality preserving multimodal discriminative learning for supervised feature selection

Feature selection has been an important preprocessing step in high-dimensional data analysis and pattern recognition. In this paper, we propose a locality preserving multimodal discriminative learning method called LPMDL for supervised feature selection, which arises by solving two standard eigenvalue problems and seeks to find a pair of optimal transformations for two sets of multivariate data in different classes. This topic can optimally discover the local structure information of the given data hided in the original space and aims at structuring an effective low-dimensional embedding space, under which LPMDL keeps nearby data pairs in the same class close and between-class data pairs apart, and the projections of the original data in different classes can be appropriately separated from each other. LPMDL can be performed either in the input space or the reproducing kernel Hilbert space which gives rise to the kernelized version of LPMDL. We also evaluate the feasibility and efficiency of the LPMDL approach by conducting extensive data visualization and classification tasks. Experimental results on a broad range of data sets show LPMDL tends to capture the intrinsic structure characteristics of the samples data due to the effective representation of the points and achieves similar or even better performance than the conventional PCA, NPE, LPP and LFDA methods.     

Color segmentation for text extraction

The capability of extracting and recognizing characters printed in color documents will widen immensely the applications of OCR systems. This paper describes a new method of color segmentation to extract character areas from a color document. At first glance, the characters seem to be printed in a single color, but actual measurements reveal that the color image has a distribution of components. Compared with clustering algorithms, our method prevents oversegmentation and fusion with the background while maintaining real-time usability. It extracts the representative colors based on a histogram analysis of the color space. Our method also contains a selective local color averaging technique that removes the problem of mesh noise on high-resolution color images.Received: 25 July 2003, Revised: 10 August 2003, Published online: 6 February 2004Correspondence to: Hiroyuki Hase. Current address: 3-9-1 Bunkyo, Fukui-shi 910-8507, Japan     

Multi-type skin diseases classification using OP-DNN based feature extraction approach

Multimedia Tools and Applications - In the current world, the disorders occurring in dermatological images are among the foremost widespread diseases. Despite being common, its identification is...     

Correction to: Video sequence feature extraction and segmentation using likelihood regression model

Multimedia Tools and Applications -