nils-herrmann/clean_scirepeval huggingface dataset:
Multi-view face pose estimation based on supervised ISA learning

Independent subspace analysis (ISA) is able to learn view-subspaces unsupervisedly from (view-unlabeled) multi-view face examples (S.Z. Li et al., 2001). We explain underlying reasons for the emergent formation of ISA view-subspaces. Based on the analysis, we present a supervised method for more effective learning of view-subspace, assuming that view-labeled face examples are available. The models thus learned give more accurate pose estimation than those obtained with the unsupervised ISA.
Statistical Learning of Multi-view Face Detection

A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost [1, 2] is a sequential forward search procedure using the greedy selection strategy. The premise oyered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new feature to the current set, the value of the performance criterion does not decrease, is violated. FloatBoost incorporates the idea of Floating Search  into AdaBoost to solve the non-monotonicity problem encountered in the sequential search of AdaBoost.We then present a system which learns to detect multi-view faces using FloatBoost. The system uses a coarse-to-fine, simple-to-complex architecture called detector-pyramid. FloatBoost learns the component detectors in the pyramid and yields similar or higher classification accuracy than AdaBoost with a smaller number of weak classifiers. This work leads to the first real-time multi-view face detection system in the world. It runs at 200 ms per image of size 320x240 pixels on a Pentium-III CPU of 700 MHz. A live demo will be shown at the conference.
A novel Bayesian shape model for facial feature extraction

This paper presents a novel application of the Bayesian shape model (BSM) for facial feature extraction. First, a full-face model is designed to describe the shape of a face, and the PCA is used to estimate the shape variance of the face model. Then, the BSM is applied to match and extract the face patch from input face images. Finally, using the face model, the extracted face patches are easily warped or normalized to a standard view. Applications of this facial feature extraction algorithm include face recognition, face video coding and retrieval, face animation and multimedia.
FloatBoost Learning for Classification

AdaBoost  minimizes an upper error bound which is an exponential function of the margin on the training set . However, the ultimate goal in applications of pattern classification is always minimum error rate. On the other hand, AdaBoost needs an effective procedure for learning weak classifiers, which by itself is difficult especially for high dimensional data. In this paper, we present a novel procedure, called FloatBoost, for learning a better boosted classifier. FloatBoost uses a backtrack mechanism after each iteration of AdaBoost to remove weak classifiers which cause higher error rates. The resulting float-boosted classifier consists of fewer weak classifiers yet achieves lower error rates than AdaBoost in both training and test. We also propose a statistical model for learning weak classifiers, based on a stagewise approximation of the posterior using an overcomplete set of scalar features. Experimental comparisons of FloatBoost and AdaBoost are provided through a difficult classification problem, face detection, where the goal is to learn from training examples a highly nonlinear classifier to differentiate between face and nonface patterns in a high dimensional space. The results clearly demonstrate the promises made by FloatBoost over AdaBoost.
Local non-negative matrix factorization as a visual representation

Proposes a novel method, called local non-negative matrix factorization (LNMF), for learning a spatially localized, parts-based subspace representation of visual patterns. An objective function is defined to impose the localization constraint, in addition to the non-negativity constraint in the standard non-negative matrix factorization (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 basis components. Experimental results are presented to compare LNMF with the NMF and principal component analysis (PCA) methods for face representation and recognition, which demonstrates the advantages of LNMF. Based on our LNMF approach, a set of orthogonal, binary, localized basis components are learned from a well-aligned face image database. It leads to a Walsh function-based representation of the face images. These properties can be used to resolve the occlusion problem, improve the computing efficiency and compress the storage requirements of a face detection and recognition system.
Audio textures

In this paper, we introduce a new audio medium, called audio texture, as a means of synthesizing long audio stream according to a given short example audio clip. The example clip is analyzed, and basic building patterns are extracted. Then an audio stream of arbitrary length is synthesized using a sequence of extracted building patterns. The patterns can be varied in the synthesis process to add variations to the generated sound. Audio textures are useful in applications such as background music, lullabies, game music, and screen saver sounds. A method is proposed for implementing audio textures. Preliminary results of audio textures are provided at our website for evaluation.
Real-time multi-view face detection

We present a detector-pyramid architecture for real-time multi-view face detection. Using a coarse to fine strategy, the full view is partitioned into finer and finer views. Each face detector in the pyramid detects faces of its respective view range. Its training is performed by using a new meta booting learning algorithm. This results in the first real-time multi-view face detection system which runs at 5 frames per second for 320/spl times/240 image sequence.
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A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost [1, 2] is a sequential forward search procedure using the greedy selection strategy. The premise oÿered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new feature to the current set, the value of the performance criterion does not decrease, is violated. FloatBoost incorporates the idea of Floating Search  into AdaBoost to solve the non-monotonicity problem encountered in the sequential search of AdaBoost. We then present a system which learns to detect multi-view faces using FloatBoost. The system uses a coarse-to-þne, simple-to-complex architecture called detector-pyramid. FloatBoost learns the component detectors in the pyramid and yields similar or higher classiþcation accuracy than AdaBoost with a smaller number of weak classiþers. This work leads to the þrst real-time multi-view face detection system in the world. It runs at 200 ms per image of size 320x240 pixels on a Pentium-III CPU of 700 MHz. A live demo will be shown at the conference.
Learning to detect multi-view faces in real-time

In this paper, we present a system which learns to detect multi-view faces. The system uses a coarse-to-fine, simple-to-complex architecture called detector-pyramid. A new boosting algorithm, called FloatBoost, is proposed to construct a strong face-nonface classifier from weak classifiers for the component detectors in the pyramid. FloatBoost incorporates the idea of Floating Search into AdaBoost, and yields similar or higher classification accuracy than AdaBoost with a smaller number of weak classifiers. This work leads to the first real-time multi-view face detection system in the world. It runs at 200 ms per image of size 320/spl times/240 pixels on a Pentium-III CPU of 700 MHz.
A Performance Evaluation Protocol for Content-Based Image Retrieval Algorithms/Systems

In this paper, we address an important yet controversial issue in content-based image retrieval (CBIR): performance evaluation (PE). The goal is to develop an objective and comprehensive protocol for quantitative evaluation and comparison of CBIR performances among different approaches. The protocol consists of two elements: acquisition of appropriate ground truth data, and definition of quantitative performance metrics. A large image data set with human-label ground truths has been built as part of the protocol and made available for public use. An application of this protocol to our MiAlbum system is also presented, which shows suitability of such a protocol.
Analysis of speaker variability

Analysis and modeling of speaker variability, such as gender, accent, age, speech rate, and phones realizations, are important issues in speech recognition. It is known that existing feature representations describing speaker variations can be of very high dimension. In this paper, we introduce two powerful multivariate statistical analysis methods, namely, principal component analysis (PCA) and independent component analysis (ICA), as tools for analysis of such variability and extraction of low dimensional feature representation. Our findings are the following: (1) the first two principal components correspond to the gender and accent, respectively. The result that the second component corresponding to the accent has never been reported before, to the best of our knowledge. (2) It is shown that ICA based features yield better classification performance than PCA ones. Using 2dimensional ICA representation, we achieved about 6.1% and 13.3% error rate in gender and accent classification, respectively, for 980 speakers.
Pairwise face recognition

We develop a pairwise classification framework for face recognition, in which a C class face recognition problem is divided into a set of C(C-1)/2 two class problems. Such a problem decomposition not only leads to a set of simpler classification problems to be solved, thereby increasing overall classification accuracy, but also provides a framework for independent feature selection for each pair of classes. A simple feature ranking strategy is used to select a small subset of the features for each pair of classes. Furthermore, we evaluate two classification methods under the pairwise comparison framework: the Bayes classifier and the AdaBoost. Experiments on a large face database with 1079 face images of 137 individuals indicate that 20 features are enough to achieve a relatively high recognition accuracy, which demonstrates the effectiveness of the pairwise recognition framework.
View-subspace analysis of multi-view face patterns

Multi-view face detection and recognition has been a challenging problem. The challenge is due to the fact that the distribution of multi-view faces in a feature space is more dispersed and more complicated than that of frontal faces. This paper presents an investigation into several view-subspace representations of multi-view faces: learning by using independent component analysis (ICA), independent subspace analysis (ISA) and topographic independent component analysis (TICA). It is shown that view-specific basis components can be learned from multi-view face examples in an unsupervised way by using ICA, ISA and TICA; whereas the components learned by using principal component analysis reveal little view-related information. The learned results provide sensible basis for constructing view-subspaces for multi-view faces. Comparative experiments demonstrate distinctive properties of ICA, ISA and TICA results, and the suitability of the results as representations of multi-view faces.
Learning Low Dimensional Invariant Signature of 3-D Object under Varying View and Illumination from 2-D Appearances

In this papel; we propose an invariant signature representation for appearances of 3-0 object under varying view and illumination, and a method for learning the signature from multi-view appearance examples. The signature, a nonlinearfeature, provides a good basis for 3-0 object detection and pose estimation due to its following properties: (1) Its location in the signature feature space is a simple function of the view and is insensitive or invariant to illuniination. ( 2 ) It changes continuously as the view changes, so that the object appearances at all possible views should constitute a known simple curve segment (manifold) in the feature space. (3) The Coordinates of the object appearances in the feature spuce ure correlated in a known way according to a predefined function of the view. The j r s t hvo properties provide a basis for object detection and the third for view (pose) estimation. To compute the signature representation from input, we present a nonlinear regression method for learning a nonlinear mapping from the input (e.g. image) space to the feature space. The ideas of the signature representation and the learning method are illustrated with experimental results for the object of human face. It is shown that the face object can be effectively modeled compactly in a IO-D nonlinear feature space. The 10-D signature presents excellent insensitivity to changes in illumination for any view. The correlation of the signature coordinates is well determined by the predefined parametric function. Applications of the proposed method in face detection and pose estimation are demonstrated.
Extraction of feature subspaces for content-based retrieval using relevance feedback

In the past few years, relevance feedback (RF) has been used as an effective solution for content-based image retrieval (CBIR). Although effective, the RF-CBIR framework does not address the issue of feature extraction for dimension reduction and noise reduction. In this paper, we propose a novel method for extracting features for the class of images represented by the positive images provided by subjective RF. Principal Component Analysis (PCA) is used to reduce both noise contained in the original image features and dimensionality of feature spaces. The method increases the retrieval speed and reduces the memory significantly without sacrificing the retrieval accuracy.
Learning low dimensional invariant signature of 3-D object under varying view and illumination from 2-D appearances

In this paper, we propose an invariant signature representation for appearances of 3-D object under varying view and illumination, and a method for learning the signature from multi-view appearance examples. The signature, a nonlinear feature, provides a good basis for 3-D object detection and pose estimation due to its following properties. (I) Its location in the signature feature space is a simple function of the view and is insensitive or invariant to illumination. (2) It changes continuously as the view changes, so that the object appearances at all possible views should constitute a known simple curve segment (manifold) in the feature space. (3) The coordinates of rite object appearances in the feature space are correlated in a known way according to a predefined function of the view. The first two properties provide a basis for object detection and the third for view (pose) estimation. To compute the signature representation from input, we present a nonlinear regression method for learning a nonlinear mapping from the input (e.g. image) space to the feature space. The ideas of the signature representation and the learning method are illustrated with experimental results for the object of human face. It is shown that the face object can be effectively, modeled compactly in a 10-D nonlinear feature space. The 10-D signature presents excellent insensitivity to changes in illumination for any view. The correlation of the signature coordinates is well determined by the predefined parametric function. Applications of the proposed method in face detection and pose estimation are demonstrated.
Kernel machine based learning for multi-view face detection and pose estimation

Face images are subject to changes in view and illumination. Such changes cause data distribution to be highly nonlinear and complex in the image space. It is desirable to learn a nonlinear mapping from the image space to a low dimensional space such that the distribution becomes simpler tighter and therefore more predictable for better modeling effaces. In this paper we present a kernel machine based approach for learning such nonlinear mappings. The aim is to provide an effective view-based representation for multi-view face detection and pose estimation. Assuming that the view is partitioned into a number of distinct ranges, one nonlinear view-subspace is learned for each (range of) view from a set of example face images of that view (range), by using kernel principal component analysis (KPCA). Projections of the data onto the view-subspaces are then computed as view-based nonlinear features. Multi-view face detection and pose estimation are performed by classifying a face into one of the facial views or into the nonface class, by using a multi-class kernel support vector classifier (KSVC). Experimental results show that fusion of evidences from multi-views can produce better results than using the result from a single view; and that our approach yields high detection and low false alarm rates in face detection and good accuracy in pose estimation, in comparison with the linear counterpart composed of linear principal component analysis (PCA) feature extraction and Fisher linear discriminant based classification (FLDC).
Learning representative local features for face detection

This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are learnt from the training set instead of arbitrarily defined. The learning procedure consists of two steps. First, a modified version of NMF (non-negative matrix factorization), namely local NMF (LNMF), is applied to obtain an overcomplete set of local features. Second, a learning algorithm based on AdaBoost is used to select a small number of local features and yields extremely efficient classifiers. Experiments are presented which show that face detection performance is comparable to state-of-the-art face detection systems.
Rule mining with prior knowledge - a belief networks approach

Some existing data mining methods, such as classification trees, neural networks and association rules, have the drawbacks that the user's prior knowledge cannot be easily specified and incorporated into the knowledge discovery process, and the rules mined from databases lack quantitative analyses. In this paper, we propose a belief networks method for rule mining, which takes the advantage of belief networks as the directed acyclic graph language and their function for numerical representation of probabilistic dependencies among the variables in the database, so that it can overcome the drawbacks. Since belief networks provide a natural representation for capturing causal relationship among a set of variables, our proposed method can mine more general correlation rules which can capture the relationship of more than two attribute variables. The potential application of the proposed method is demonstrated through the detailed case studies on benchmark databases.
Boosting for content-based audio classification and retrieval: an evaluation

In this paper, we evaluate a recently proposed algorithm in machine learning called AdaBoost for content-based audio classification and retrieval. AdaBoost is a kind of large margin classifiers and is efficient for on-line learning. Our focus is to evaluate its classification and retrieval accuracy as compared with other methods. The Muscle Fish audio database of 409 sounds is used for the evaluation with perceptual and cepstral features.
Content-based audio segmentation using support vector machines

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.
Learning topographic representation for multi-view image patterns

In 3D object detection and recognition, the object of interest in an image is subject to changes in view-point as well as illumination. It is benefit for the detection and recognition if a representation can be derived to account for view and illumination changes in an effective and meaningful way. In this paper, we propose a method for learning such a representation from a set of un-labeled images containing the appearances of the object viewed from various poses and in various illuminations. Topographic Independent Component Analysis (TICA) is applied for the unsupervised learning to produce an emergent result, that is a topographic map of basis components. The map is topographic in the following sense: the basis components as the units of the map are ordered in the 2D map such that components of similar viewing angle are group in one axis and changes in illumination are accounted for in the other axis. This provides a meaningful set of basis vectors that may be used to construct view subspaces for appearance based multi-view object detection and recognition.
Learning probabilistic distribution model for multi-view face detection

Modeling subspaces of a distribution of interest in high dimensional spaces is a challenging problem in pattern analysis. In this paper, we present a novel framework for pose invariant face detection through. multi-view face distribution modeling. The approach is aimed to learn a set of low-dimensional subspaces from an originally nonlinear distribution by using the mixtures of probabilistic PCA. From the experiments, we found the learned PPCA models are of low dimensionality and exhibit high local linearity, and consequently offer an efficient representation for visual recognition. The model is then used to extract features and select "representative" negative training samples. Multi-view face detection is performed in the derived feature space by classifying each face into one of the view classes or into the nonface class, by using a multi-class SVM array classifier. The classification results from each view are fused together and yields the final classification results. The experimental results demonstrate the performance superiority of our proposed framework while performing multi-view face detection.