nils-herrmann/clean_scirepeval huggingface dataset:
Content-Based Audio Segmentation Content-Based Audio Segmentation

Audio exists at everywhere, but is often out-of-order. It is necessary to arrange them into regularized classes in order to use them more easily. It is also useful, especially in video content analysis, to segment an audio stream according to audio types. In this paper, we present our work in applying support vector machines (SVMs) in audio segmentation and classification. Five audio classes are considered: silence, music, background sound, pure speech, and non-pure speech which includes speech over music and speech over noise. A SVM learns optimal class boundaries from training data to best separate between two classes. A sound clip is segmented by classifying each sub-clip of one second into one of these five classes. Experiments on a database composed of clips of 14870 seconds in total length show that the average accuracy rate for the SVM method is much better than that of the traditional Euclidean distance based (nearest neighbor) method.
Content-based retrieval of video shot using the-improved nearest feature line method

Shot-based classification and retrieval is very important for video database organization and access. We present a new approach: 'nearest feature line - NFL' used in shot retrieval. We look at key-frames in a shot as feature points to represent the shot in feature space. Lines connecting the feature points are further used to approximate the variations in the whole shot. The similarity between the query image and the shots in video database are measured by calculating the distance between the query image and the feature lines in feature space. To make it more suited to video data, we improved the original NFL method by adding constraints on the feature lines. Experimental results show that our improved NFL method is better than the traditional classification methods such as nearest neighbor (NN) and nearest center (NC).
Oxidation Behavior of a Ti3Al–Nb Alloy with Surface Thin Oxide Films

Thin films of aluminum, cerium, and yttrium oxides were applied onto the surfaces of Ti3Al–11Nb samples using an electrodeposition technique. The oxidation behaviors of the Ti3Al–Nb alloy, with and without these surface-applied films, were studied in air at 800–1000°C. The results showed that the oxidation rate of the alloy can be reduced by Ce oxide and Y oxide films, and the greatest improvement comes from oxidation of the Y oxide-coated specimens at 800°C. With increasing oxidation temperature, the difference between the Co-oxide and Y-oxide films becomes smaller. The results also indicated that the Ce-oxide and Y-oxide films can significantly improve the oxide scale-spallation resistance. On the other hand, Al-oxide films result in detrimental effects on the oxidation and scale-spallation resistance of the Ti3Al–Nb alloy. Based on the experimental results, the effects of the different surface films on the oxidation mechanism are discussed.
Distance-from-boundary as a metric for texture image retrieval

A new metric is proposed for texture image retrieval, which is based on the signed distance of the images in the database to a boundary chosen by the query. This novel metric has three advantages: (1) the boundary distance measures are relatively insensitive to the sample distributions; (2) the same retrieval results can be obtained with respect to different (but visually similar) queries; (3) retrieval performance can be improved. The boundaries are obtained by using a statistical learning algorithm called support vector machine (SVM), and hence the boundaries can be simply represented by some vectors and their combination coefficients. Experimental results on the Brodatz texture database indicate that a significantly better retrieval performance can be achieved as compared to the traditional Euclidean distance-based approach. This technique can be further developed to learn pattern similarities among different texture classes and used in relevance feedback.
Direct appearance models

Active appearance model (AAM), which makes ingenious use of both shape and texture constraints, is a powerful tool for face modeling, alignment and facial feature extraction under shape deformations and texture variations. However, as we show through our analysis and experiments, there exist admissible appearances that are not modeled by AAM and hence cannot be reached by AAM search; also the mapping from the texture subspace to the shape subspace is many-to-one and therefore a shape should be determined entirely by the texture in it. We propose a new appearance model, called direct appearance model (DAM), without combining from shape and texture as in AAM. The DAM model uses texture information directly in the prediction of the shape and in the estimation of position and appearance (hence the name DAM). In addition, DAM predicts the new face position and appearance based on principal components of texture difference vectors, instead of the raw vectors themselves as in AAM. These lead to the following advantages over AAM: (1) DAM subspaces include admissible appearances previously unseen in AAM, (2) convergence and accuracy are improved, and (3) memory requirement is cut down to a large extent. The advantages are substantiated by comparative experimental results.
Markov Random Field Modeling in Image Analysis

Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables systematic development of optimal vision algorithms when used with optimization principles. This detailed and thoroughly enhanced third edition presents a comprehensive study / reference to theories, methodologies and recent developments in solving computer vision problems based on MRFs, statistics and optimisation. It treats various problems in low- and high-level computational vision in a systematic and unified way within the MAP-MRF framework. Among the main issues covered are: how to use MRFs to encode contextual constraints that are indispensable to image understanding; how to derive the objective function for the optimal solution to a problem; and how to design computational algorithms for finding an optimal solution. Easy-to-follow and coherent, the revised edition is accessible, includes the most recent advances, and has new and expanded sections on such topics as: Discriminative Random Fields (DRF) Strong Random Fields (SRF) Spatial-Temporal Models Total Variation Models Learning MRF for Classification (motivation + DRF) Relation to Graphic Models Graph Cuts Belief Propagation Features: Focuses on the application of Markov random fields to computer vision problems, such as image restoration and edge detection in the low-level domain, and object matching and recognition in the high-level domain Presents various vision models in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation Uses a variety of examples to illustrate how to convert a specific vision problem involving uncertainties and constraints into essentially an optimization problem under the MRF setting Introduces readers to the basic concepts, important models and various special classes of MRFs on the regular image lattice and MRFs on relational graphs derived from images Examines the problems of parameter estimation and function optimization Includes an extensive list of references This broad-ranging and comprehensive volume is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It has been class-tested and is suitable as a textbook for advanced courses relating to these areas.
Facial feature extraction and image warping using PCA based statistic model

A new algorithm is proposed to extract the facial features and estimate the control points for facial image warping using the principle component analysis (PCA) based statistic face model. In this algorithm, first a full-face model consisting the contour points and the control points is built. Based on a number of manually marked training samples, the prior distribution of the full-face model can be obtained by using the PCA. Given an input face image, first the contour points are obtained by using the Bayesian shape model (BSM), and then the control points are estimated from the contour points. Finally, the extracted face path is normalized using the piece-wise affine triangle warping algorithm. Experimental results illustrate the effectiveness of the proposed algorithm.
Learning spatially localized, parts-based representation

In this paper, we propose a novel method, called local non-negative matrix factorization (LNMF), for learning spatially localized, parts-based subspace representation of visual patterns. An objective function is defined to impose a localization constraint, in addition to the non-negativity constraint in the standard NMF. This gives a set of bases which not only allows a non-subtractive (part-based) representation of images but also manifests localized features. An algorithm is presented for the learning of such basic components. Experimental results are presented to compare LNMF with the NMF and PCA methods for face representation and recognition, which demonstrates advantages of LNMF.
View-based clustering of object appearances based on independent subspace analysis

In 3D object detection and recognition, an object of interest is subject to changes in view as well as in illumination and shape. For image classification purpose, it is desirable to derive a representation in which intrinsic characteristics of the object are captured in a low dimensional space while effects due to artifacts are reduced. In this paper, we propose a method for view-based unsupervised learning of object appearances. First, view-subspaces are learned from a view-unlabeled data set of multi-view appearances, using independent subspace analysis (ISA). A learned view-subspace provides a representation of appearances at that view, regardless of illumination effect. A measure, called view-subspace activity, is calculated thereby to provide a metric for view-based classification. View-based clustering is then performed by using maximum view-subspace activity (MVSA) criterion. This work is to the best of our knowledge the first devoted research on view-based clustering of images.
Real-Time MultiView Face Detection , Tracking , Pose Estimation , Alignment , and Recognition ( Updated Dec 1 , 2001 )

We have developed a system based on technologies resulting from our recent research in multi-view face detection and tracking [1, 2], pose estimation , alignment [4, 5, 6], and recognition [7]; where multi-view means out-of-plane rotations in [0Æ; 180Æ] left-right (90 corresponding to the frontal view) and in 30Æ up-down. The system, whose structure is shown in Fig.1, takes gray level static images or video as the input, without using color or motion information, and finds the locations, sizes, poses, and identities of faces in the input. It also includes internal feedback mechanisms for collecting and adding to the training sets new face examples for re-training the performing modules. Our objective has been to make these components work practically, fast and robustly. The methodologies used to achieve this for multi-view faces have evolved from our previous expensive nonlinear models [8, 9] to the current efficient linear models for face detection and alignment [1, 5], and from the previous unsupervised learning [10, 11, 12] to current supervised learning  for pose estimation. The mpeg clips for these demos can be downloaded at http://research.microsoft.com/ szli/Demos/CVPR01.html.
Learning Similarity for Texture Image Retrieval

A novel algorithm is proposed to learn pattern similarities for texture image retrieval. Similar patterns in different texture classes are grouped into a cluster in the feature space. Each cluster is isolated from others by an enclosed boundary, which is represented by several support vectors and their weights obtained from a statistical learning algorithm called support vector machine (SVM). The signed distance of a pattern to the boundary is used to measure its similarity. Furthermore, the patterns of different classes within each cluster are separated by several sub-boundaries, which are also learned by the SVMs. The signed distances of the similar patterns to a particular sub-boundary associated with the query image are used for ranking these patterns. Experimental results on the Brodatz texture database indicate that the new method performs significantly better than the traditional Euclidean distance based approach.
Face detection using neural learning with optimized feature set

A system using feature optimization and neural learning techniques is presented for detection of upright fronta l faces. The design of the system takes advantages of the existing techniques and avoid their shortcomings. Statistic al pattern recognition (PR) techniques are used to optimize th e feature selection. A neural network (NN) method is used to learn a complex mapping function for the classification, given the optimized feature set. The optimization of feature set reduces the burden of the subsequent NN classifier and improves its performance in learning speed and classification rates. The use of the NN for classification avoids the need for the simplification of classifier function, as pra cticed in the PR approach, for the mathematical tractability at the sacrifice of the performance. Experimental results show that our system produces higher detection and lower missing rates than several existing state-of-the-art face dete ction systems, with an average false detection rate.
A Bayesian Model for Extracting Facial Features

A Bayesian Model (BM) is proposed in this paper for extracting facial features. In the BM, first the prior distribution of object shapes, which reflects the global shape variations of the object contour, is estimated from the sample data. This distribution is then utilized to constrain and dynamically adjust the prototype contour in the matching procedure, in this way large or global shape deformations due to the variations of samples can be tolerated. Moreover, a transformational invariant internal energy term is introduced to describe mainly the local shape deformations between the prototype contour in the shape domain and the deformable contour in the image domain, so that the proposed BM can match the objects undergoing not only global but also local variations. Experiment results based on real facial feature extraction demonstrate that the BM is more robust and insensitive to the positions, viewpoints, and deformations of object shapes, as compared to the Active Shape Model (ASM) algorithm.
Roof-edge preserving image smoothing based on MRFs

A novel Markov random field (MRF) model is proposed for roof-edge (as well as step-edge) preserving image smoothing. Image surfaces containing roof-edges are represented by piecewise continuous polynomial functions governed by a few parameters. Piecewise smoothness constraint is imposed on these parameters rather than on the surface heights as is in traditional models for step-edges. In this way, roof edges are preserved without the necessity to deal with unstable higher order derivatives.
Face recognition based on multiple facial features

An automatic face recognition system based on multiple facial features is described. Each facial feature is represented by a Gabor-based complex vector and is localized by an automatic facial feature detection scheme. Two face recognition approaches, named two-layer nearest neighbor (TLNN) and modular nearest feature line (MNFL) respectively, are proposed. Both TLNN and MNFL are based on the multiple facial features detected for each image and their superiority in face recognition is demonstrated.
Error Resilience Methods for FGS Video Enhancement Bitstream

Fine Granularity Scalable (FGS) video coding scheme that has been recently adopted in the MPEG-4 streaming video profile provides a layered and fine granularity scalable bitstream with different importance at different layers. This property makes FGS bitstream suitable for transmitting over the error-prone channels with unequal error protection. However, there are no tools currently available for error resilience in the FGS enhancement bitstream. This causes two problems when delivering the enhancement bitstream through an error-prone channel. First, there is only a weak error detection capability in the enhancement layer. The decoder would put a lot of wrong data into the decoded video before it knows an error has occured in the bitstream. Secondly, in the current FGS scheme, once the decoder detects an error, it would discard the rest of the bitstream in that frame. The effective transmitted video data will be determined by the channel error rate, but not by the channel bandwidth. In this paper, we propose to include some error-resilience tools into the FGS enhancement layer bitstream. We design a hierarchical enhancement layer bitstream structure with resynchronization markers and Header Extension Code (HEC). The experimental results show that the proposed enhancement layer bitstream has stronger error detection and resynchronization capabilities when transmitted over error-prone channels. Thus it provides much better decoded video.
Face recognition by support vector machines

Support vector machines (SVM) have been recently proposed as a new technique for pattern recognition. SVM with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVM on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. We also present the recognition experiment on a larger face database of 1079 images of 137 individuals. We compare the SVM-based recognition with the standard eigenface approach using the nearest center classification (NCC) criterion.
Modeling Image Analysis Problems Using Markov Random Fields

Modeling problems in this article are addressed mainly from the computational viewpoint. The primary concerns are how to de ne an objective function for the optimal solution for an image analysis problem and how to nd the optimal solution. The reason for de ning the solution in an optimization sense is due to various uncertainties in imaging processes. It may be diÆcult to nd the perfect solution, so we usually look for an optimal one in the sense that an objective, into which constraints are encoded, is optimized.
Key-frame extraction and shot retrieval using nearest feature line (NFL)

Query by key frame or video example is a convenient and often effective way to search in video database. This paper proposes a new approach to support such searches. The main contribution of the proposed approach is the consideration of both feature extraction and distance computation as a whole process. With a video shot represented by key-frames corresponding to feature points in a feature space, a new metric is defined to measure the distance between a query image and a shot based on the concept of Nearest Feature Line (NFL). We propose to use the “breakpoints” of feature trajectory of a video shot as the key frames and use the lines passing through these points to represent the shot. When combined with the NFL method, it helps to achieve a better performance, as evidenced by experiments.
A theoretical justification of nearest feature line method

A novel pattern classification method, called the nearest feature line (NFL), has been proposed by Li (1999). The NFL provides a better alternative to the popular nearest neighbor (NN) classifier when multiple prototypes per class are available. It has been shown to achieve consistently better performance than the NN in terms of the error rate with simulated data as well as real application data. This paper gives a theoretical justification of the NFL. The main result is a proof that the NFL can achieve lower probabilistic error than the NN when the number of available prototypes for each class is finite and the dimension of a feature space is high. A simulation experiment shows that the NFL produces considerably lower error rate than the NN.
Efficient object matching using affine-invariant deformable contour

An affine-invariant deformable contour model for object matching, called affine-invariant eigensnake (AI-ES), is presented in the Bayesian framework. In AI-ES, the prior distribution of object shapes is estimated and utilized to constrain the prototype contour, which is dynamically adjustable in the matching process. Also, an affine-invariant internal energy is presented to define the global and local shape deformation of the contours between the shape domain and the image domain. Experiments on real object matching show that the proposed method is more robust and insensitive to the positions, viewpoints, and large deformations of object shapes, than the active shape model (ASM) and the AI-snake model.
Content-based audio classification and retrieval using the nearest feature line method

A method is presented for content-based audio classification and retrieval. It is based on a new pattern classification method called the nearest feature line (NFL). In the NFL, information provided by multiple prototypes per class is explored. This contrasts to the nearest neighbor (NN) classification in which the query is compared to each prototype individually. Regarding audio representation, perceptual and cepstral features and their combinations are considered. Extensive experiments are performed to compare various classification methods and feature sets. The results show that the NFL-based method produces consistently better results than the NN-based and other methods. A system resulting from this work has achieved the error rate of 9.78%, as compared to that of 18.34% of a compelling existing system, as tested on a common audio database.
Combining feature optimization into neural network based face detection

A system using feature optimization and neural learning techniques is presented for detection of upright frontal faces. The feature set is optimized in terms of the Fisher's linear discrimination (FLD) criterion. A neural network (NN) method is used to learn a complex mapping function for the classification, given the optimized feature set. The optimization of feature set reduces the burden of the subsequent NN classifier and improves its performance in learning speed and classification rates. Experimental results show that the feature set optimized by using the FLD transform significantly improves the detection rate while maintaining the same false alarms. Our system produces higher detection and lower missing rates than several existing state-of-the-art face detection systems, with an average false detection rate.