waashk/dblp huggingface dataset:
consensus-based identification of spectral signatures for classification of high-dimensional biomedical spectra. the identification of spectral signatures is crucial for the classification/profiling of biomedical spectra. because only limited number of biomedical samples of high dimensionality is typically available, dimensionality reduction techniques (identification of discriminatory features) are essential for robust classifier development. we show, on three real-world biomedical datasets, the potential of a consensus-based identification of important feature subsets, using a genetic algorithm and a sparse linear classifier. when training data are in short supply, the proposed methodology leads to more stable subset identification and higher classification accuracy.
class separability in spaces reduced by feature selection. we investigated the geometrical complexity of several high-dimensional, small sample classification problems and its changes due to two popular feature selection procedures, forward feature selection (ffs) and linear programming support vector machine (lpsvm). we found that both procedures are able to transform the problems to spaces of very low dimensionality where class separability is improved over that in the original space. the study shows that geometrical complexities have good potentials for comparing different feature selection methods in aspects relevant to classification accuracy, yet independent of particular classifier choices.
feature subset selection using ica for classifying emphysema in hrct images. feature subset selection, applied as a pre-processing step to machine learning, is valuable in dimensionality reduction, eliminating irrelevant data and improving classifier performance. in recent years, data in some applications has increased in both the number of instances and features. it is in this context that we introduce a novel approach to reduce both instance and feature space through independent component analysis (ica) for the classification of emphysema in high resolution computer tomography (hrct) images. the technique was tested successfully on 60 hrct scans having emphysema using three different classifiers (naïve bayes, c4.5 and seeded k means). the results were also compared against "density mask'', a standard approach used for emphysema detection in medical image analysis. in addition, the results were visually validated by radiologists.
fast dynamic mosaicing and person following. a system for video surveillance purposes in wide areas based on active cameras, also capable to follow a person in the scene by keeping him framed, is presented. the proposed approach is based on the so-called direction histograms to compute the ego-motion and on frame differencing for detecting moving objects. it exploits post-processing and active contours to extract precise shape of moving objects to be fed to a probabilistic algorithm to track moving people in the scene. person following, instead, is based on simple heuristic rules that move the camera as soon as the selected person is close to the border of the field of view. experimental results on a live active camera demonstrate the feasibility of real-time person following.
an adaptive classification algorithm using robust incremental clustering. in this paper we present an adaptive classification method that features a robust, efficient and simple to use incremental clustering algorithm. a new assignment strategy for incorporating new data patterns allows clusters to align more exhaustively with the data structure. this almost eliminates the sensitivity to the order of input data, many incremental clustering algorithms suffer from, reduces the number of clusters needed and thus improves also time efficiency. for updating the clusters' representations we utilize an incremental version of pca which generates its learning rate automatically from the number of patterns. furthermore, the size and number of clusters is controlled by the classification error. so we get a classification method where nothing but the target error needs to be pre-specified. we conducted experiments on artificial and real data to demonstrate the capabilities of the proposed algorithm.
model-free augmented reality by virtual visual servoing. this paper presents a method based on the virtual visual servoing approach [10] to achieve markerless augmented reality applications. this work aims to realize this task using as little prior 3d information as possible. virtual visual servoing techniques that lead to a non-linear minimization approach allow one to estimate the 2d transformation between two images of a video sequence which permits to achieve augmented reality on this sequence. thanks to the work that has already been carried out in this domain, the presented method is efficient and robust wrt. noise and occlusions. it allows very realistic augmented videos with minimum knowledge about the real environment.
parameter tuning using the out-of-bootstrap generalisation error estimate for stochastic discrimination and random forests. stochastic discrimination is a machine learning algorithm with strong theoretical underpinnings and good published results on uci datasets. however, it has not been popular amongst practitioners. we look at some of the issues involved in its use, propose the out-of-bootstrap error estimator as a means of tuning stochastic discrimination's and other classifiers' performance and contrast stochastic discrimination's utility with that of a related classification technique of random forests.
a method for the identification of noisy regions in normalized iris images. in this paper we propose a new method for the identification of noisy regions in normalized iris images. starting from a normalized and dimensionless iris image in the polar coordinate system, our goal consists in the classification of every pixel as "noise" or "not noise". this classification could be helpful in the posterior feature extraction or feature comparison stages regarding the construction of biometric iris signatures more robust to noise. we propose the extraction of 8 well known features for each pixel of the images followed by the classification through a neural network.
toward blind robust watermarking of vector maps. digital watermarking technology is applied on multimedia data such as images, audio and video etc., for copyright protection and temper proofing. vector type images are commonly employed in cartography or geographical information system, and their copyright or ownership must be protected. previous works on digital watermarking of vector maps adopted the informed or non-blind method, which needs the original maps for watermark detection. in this paper, we propose a novel and blind watermarking approach, which firstly subdivides the map into mesh segments, and then embed the watermark in each segment. in the detection step, either map segmentation or watermark detection doesn't need the original map. simulation results show that the proposed approach also resists to cropping and noise attacks.
migration analysis: an alternative approach for analyzing learning performance. estimated generalization error is the main index that indicates learning performance, but it is inadequate for further analysis. bias-variance theory tries to overcome the limitation of analyzing learning performance, but the concept of bias-variance is still controversial when applied to the classification problem. in this paper, we propose a new alternative, simple and practical, analytical method called 'migration analysis' to analyze the learning results. we compare the properties of migration analysis to bias-variance framework, and use it to analyze two so-called ensemble learners: bagging and adaboost. the results not only explain these ensemble learners in different ways, but also shed light to the new promising learning algorithm.
real-time camera tracking using known 3d models and a particle filter. we present an algorithm which can track the 3d pose of a hand held camera in real-time using predefined models of objects in the scene. the technique utilises and extends recently developed techniques for 3d tracking with a particle filter. the novelty is in the use of edge information for 3d tracking which has not been achieved before within a realtime bayesian sampling framework. we develop a robust tracker by carefully designing the particle filter observation model: grouping line segments from a known model into 3d junctions and performing fast inlier/outlier counts on projected junction branches. results demonstrate the ability to track full 3d pose in dense clutter whilst using a minimal number of junctions.
semantic analysis on medical images: a case study. computer aided diagnosis (cad) systems, as secondopinion assistance to radiologists, are being developed. however, the lack of semantic information at the image processing stage is expected to limit the ultimate performance of such cad systems. a solution can be provided by a specific medical domain terminology, which contains several concepts that describe features of abnormalities and the relationships (properties) between these concepts; as such is well readable by both human beings (radiologists) and machine agents (cad systems). here we investigate a specific ontology and provide a clear evaluation based on string and description logic (dl). the results provide a clear understanding of semantic medical image analysis with which cad systems are expected to improve their performance.
a bayesian approach to simultaneous motion estimation of multiple independently moving objects. in this paper, the problem of simultaneous motion estimation of multiple independently moving objects is addressed. a novel bayesian approach is designed for solving this problem using the sequential importance sampling (sis) method. in the proposed algorithm, a balancing step is added into the sis procedure to preserve samples of low weights so that all objects have enough samples to propagate empirical motion distributions. by using the proposed algorithm, the relative motions of all moving objects with respect to camera can be simultaneously estimated . this algorithm has been tested on both synthetic and real image sequences. improved results have been achieved.
affine invariant dynamic time warping and its application to online rotated handwriting recognition. dynamic time warping (dtw) has been widely used to align and compare two sequences. dtw can efficiently deal with local warp or deformation between sequences. however, it can't take account of affine transformation of sequences, such as rotation, shift and scale. this paper introduces a novel affine invariant dynamic time warping (ai-dtw) method, which tries to deal with the affine transformation and sequence alignment in a unified framework. we propose an iterative algorithm to estimate the optimal transformation matrix and warping path by mutually updating them. recognition experiments on the online rotated handwritten data illustrated that the ai-dtw achieves a recognition rate of 95.54%, which is significantly higher than that (65.87%) of the classical dtw method.
recover writing trajectory from multiple stroked image using bidirectional dynamic search. the recovery of writing trajectory from offline handwritten image is generally regarded as a difficult problem [1]. this paper introduced a method to recover the writing trajectory from multiple stroked images by searching the best matching writing paths of template strokes. the searching procedure is guided by a matching cost function which is defined as the summation of positional distortion cost and directional difference cost between the template stroke and its matching path. we develop a bidirectional search algorithm based on dynamic programming to find the best matching path. the algorithm can efficiently reduce the searching space, while hold the start/end vertex constraint. experiments on the handwritten english words and chinese characters demonstrated the effectiveness of our method.
an improved semi-supervised support vector machine based translation algorithm for bci systems. in this study, we propose an improved semi-supervised support vector machine (svm) based translation algorithm for brain-computer interface (bci) systems, aiming at reducing the time-consuming training process and enhancing the adaptability of bci systems. in this algorithm, we apply a semi-supervised svm, which builds a svm classifier based on small amounts of labeled data and large amounts of unlabeled data, to translating the features extracted from the electrical recordings of brain into control signals. for reducing the time to train the semi-supervised svm, we improve it by introducing an batch-mode incremental training method, which also can be used to enhance the adaptability of online bci systems. the off-line data analysis results demonstrated the effectiveness of our algorithm.
efficient feature extraction based on regularized uncorrelated chernoff discriminant analysis. in this paper, two regularized uncorrelated chernoff discriminant analysis (rucda) techniques are introduced. as a heteroscedastic extension of the classwise weighted fisher criterion, the class-wise weighted chernoff criterion employed in rucda better approximates the chernoff upper bound of the bayes classification error in the transformed space, which enable the resulting rucda to extract uncorrelated discriminatory information from both mean and covariance differences. experiments performed on uci benchmark and protein secondary structure datasets demonstrate good performance of the proposed technique.
unsupervised texture classification: automatically discover and classify texture patterns. in this paper, we present a novel approach to classify texture collections. this approach does not require experts to provide annotated training set. given the image collection, we extract a set of invariant descriptors from each image. the descriptors of all images are vector-quantized to form 'keypoints'. then we represent the texture images by 'bagof- keypoints' vectors. by analogy text classification, we use probabilistic latent semantic indexing(plsi) to perform unsupervised classification. the proposed approach is evaluated using the uiuc database which contains significant viewpoint and scale changes. the performances of classifying new images using the parameters learnt from the unannotated image collection are also presented. the experiment results clearly demonstrate that the approach is robust to scale and viewpoint changes, and achieves good classification accuracy even without annotated training set.
kernel neural gas algorithms with application to cluster analysis. we present a kernel neural gas (kng) algorithm, to generalize the original neural gas (ng) algorithm into a higher dimensional feature space. the proposed kng algorithm can successfully tackle nonlinearly structured datasets. compared with several existing kernel clustering algorithms, the kng can be insensitive to initializations due to employing the sequential learning strategy and the neighborhood cooperation scheme. further, a distortion sensitive kng (dskng) algorithm is proposed to tackle the imbalanced clustering problem. experimental results show that our kng algorithm can successfully deal with nonlinearly structured datasets and multi-modal datasets, while the imbalanced clusters are detected by the dskng.
a novel kernel prototype-based learning algorithm. in this paper, we propose a novel kernel prototype-based learning algorithm, called kernel generalized learning vector quantization (kglvq) algorithm, which can significantly improve the classification performance of the original generalized learning vector quantization algorithm in complex pattern classification tasks. in addition, the kglvq can also serve as a good general kernel learning framework for further investigation.
face recognition under varying lighting based on the probabilistic model of gabor phase. this paper present a novel method for robust illumination-tolerant face recognition based on the gabor phase and a probabilistic similarity measure. invited by the work in eigenphases [1] by using the phase spectrum of face images, we use the phase information of the multi-resolution and multi-orientation gabor filters. we show that the gabor phase has more discriminative information and it is tolerate to illumination variations. then we use a probabilistic similarity measure based on a bayesian (map) analysis of the difference between the gabor phases of two face images. we train the model using some images in the illumination subset of cmu-pie database and test on the other images of cmu-pie database and the yale b database and get comparative results.
tone mapping for hdr image using optimization a new closed form solution. this work studies an optimization approach for designing tone reproduction curve (trc) based tone mapping operators for the display of high dynamic range (hdr) images in low dynamic range (ldr) reproduction media. previous work has shown that the tone mapping problem can formulated as that of optimizing a two-term cost function where adjusting the relative weightings of the two terms allows users to interactively control the appearance of the output image. however, only heuristic solutions to the tone mapping objective function have been found in past research. the main contribution of this paper is the re-formulation of the objective function to allow the introduction of a closed-form solution to the two-term tone mapping objective function. the new solution has simplified previous heuristic solutions and made this approach mathematically more elegant, computationally faster and practically easier to implement.
grey scale image skeletonisation from noise-damped vector potential. this paper describes a method for curvature dependant skeletonisation in grey-scale images. we commence from a magnetostatic analogy, where the tangential edge flow (the cross product of the edge gradient and the image plane normal) is intepretted as a current. a vector potential is constructed by integrating the current weighted by inverse distance over the image plane. the skeleton corresponds to the location of valley lines in the vector potential. to damp noise effects we damp the current with an exponential function of the local curvature.
graph matching using commute time spanning trees. this paper exploits the properties of the commute time for the purposes of graph matching. our starting point is the random walk on the graph, which is determined by the heat-kernel of the graph and can be computed from the spectrum of the graph laplacian. we characterise the random walk using the commute time between nodes, and show how this quantity may be computed from the laplacian spectrum using the discrete green's function. we use the commute-time to locate the minimum spanning tree of the graph. the spanning trees located using commute time prove to be stable to structural variations. we match the graphs by applying a tree-matching method to the spanning trees. we experiment with the method on synthetic and real-world image data, where it proves to be effective.
graph matching using commute time spanning tr. this paper exploits the properties of the commute time for the purposes of graph matching. our starting point is the random walk on the graph, which is determined by the heat-kernel of the graph and can be computed from the spectrum of the graph laplacian. we characterise the random walk using the commute time between nodes, and show how this quantity may be computed from the laplacian spectrum using the discrete green's function. we use the commute-time to locate the minimum spanning tree of the graph. the spanning trees located using commute time prove to be stable to structural variations. we match the graphs by applying a tree-matching method to the spanning trees. we experiment with the method on synthetic and real-world image data, where it proves to be effective.
a hybrid watermarking scheme for h.264/avc video. a novel h.264/avc watermarking method is proposed in this paper. by embedding the robust watermark into dct domain and the fragile watermark into motion vectors respectively, the proposed method can jointly achieve both copyright protection and authentication. our scheme outperforms other video watermarking schemes on higher watermarking capacity especially in lower compression bit-rates. furthermore, being well aligned with lagrangian optimization for mode choice featured in h.264/avc, the proposed scheme only introduces small distortions into the video content. experimental results also demonstrate that the proposed solution is very computationally efficient during watermark extraction.
efficient relevance feedback using semi-supervised kernel-specified k-means clustering. in this paper, we present an efficient and convenient relevance feedback (rf) by using a semi-supervised kernel-specified kmeans clustering (skkc) technique. skkc is used to cluster the retrieval results so that rf can be conducted on the cluster level. compared with traditional rf conducted on the point/single-image level, the new rf will facilitate the rf selection and reduce user's efforts on it. furthermore, the proposed approach enables an accumulated learning ability by recording and learning from the history of users' rfs. the new rf is applied in a content-based medical image retrieval (cbmir) system. experimental results on imageclef database of around 9,000 images have shown that the proposed new rf is able to improve effectiveness and efficiency of cbmir.
spectrum analysis based onwindows with variable widths for online signature verification. in this paper, an online signature verification scheme based on spectrum analysis and mahalanobis decision is proposed. we firstly divided signatures to a number of frames with variable widths according to the characteristics of the time sequences, and then employed the fast fourier transformation(fft) to extract the spectrum of signatures. the distance between the fourier coefficient within the corresponding frames is computed, and the mahalanobis decision making is employed. experimentation demonstrates that spectrum analysis based on windows with variable widths is effective for online signature signals.
a robust method for the vietnamese handwritten and speech recognition. in this paper,we propose an approach based on hmm and linguistics for the vietnamese recognition problem, including handwritten and speech recognition.the main contribution is that our method could be used to model all vietnamese isolated words by a small number of hmms. the method is not only used for handwritten recognition but also for speech recognition.furthermore,it could be integrated with language models to improve the accuracy.experimental results show that our approach is robust and considerable.
perceptual criterion based fragile audio watermarking using adaptive wavelet packets. in this paper a novel watermarking scheme for audio authentication is proposed. during quantization process, the watermark is embedded in the wavelet packet domain. the best basis of the wavelet packets adapts to the psychoacoustic model so that the decomposed subband structure is closer to that of critical bands, which makes the embedding algorithm more flexible. unlike previous schemes, the new method embeds several bits of watermark message into one coefficient. by introducing the detecting function, the proposed scheme can authenticate the integrity of the audio signals as well as locate the tamper in time/frequency domain. effectiveness of the new approach is demonstrated by the experimental results.
data hiding in mpeg compressed audio using wet paper codes. in this paper a novel data hiding scheme for mpeg compressed audio is proposed. it exploits wet paper codes as the communication channel and hides data directly in the mpeg audio bit stream by modifying the mpeg audio quantization process. it is applicable to all three layers of mpeg audio. experimental results show that the proposed method is sustainable against transcoding and re-encoding without degrading the audio quality or increasing the file size.