nils-herrmann/clean_scirepeval huggingface dataset:
Visual-Patch-Attention-Aware Saliency Detection

The human visual system (HVS) can reliably perceive salient objects in an image, but, it remains a challenge to computationally model the process of detecting salient objects without prior knowledge of the image contents. This paper proposes a visual-attention-aware model to mimic the HVS for salient-object detection. The informative and directional patches can be seen as visual stimuli, and used as neuronal cues for humans to interpret and detect salient objects. In order to simulate this process, two typical patches are extracted individually and in parallel from the intensity channel and the discriminant color channel, respectively, as the primitives. In our algorithm, an improved wavelet-based salient-patch detector is used to extract the visually informative patches. In addition, as humans are sensitive to orientation features, and as directional patches are reliable cues, we also propose a method for extracting directional patches. These two different types of patches are then combined to form the most important patches, which are called preferential patches and are considered as the visual stimuli applied to the HVS for salient-object detection. Compared with the state-of-the-art methods for salient-object detection, experimental results using publicly available datasets show that our produced algorithm is reliable and effective.
Cost-sensitive feature selection in medical data analysis with trace ratio criterion

Feature selection and classification are important tasks in medical data mining. However, different misclassifications of medical cases could lead to different losses. This paper proposes a framework for medical data classification and relevant feature selection by the combination of the trace ratio criterion and a novel cost-sensitive linear discriminant analysis classifier approach. The proposed multi-class cost-sensitive linear discriminant analysis classifier uses linear discriminant coefficients as conditional probabilities to estimate the posterior probabilities of a testing instance, calculates misclassification losses via the posterior probabilities, and predicts the class label that minimizes losses. Experimental results showed that the proposed scheme have comparable or even lower total cost and higher accuracy than state-of-the-art cost-sensitive classification algorithm.
Efficient human face recognition based on shape and texture

Shape and texture of a human face are useful and complementary' information for face recognition. Hausdorff distance has been successfully applied for face recognition, which can measure the difference between two face images based on their respective edge representations. However, this distance measure does not consider the texture information about human faces. In this paper, a new Hausdorff distance that combines both the shape and texture information is proposed for human face recognition. The texture information is represented by means of the Gabor wavelets. This new method has the advantages of invariant to illumination and low memory requirement as Hausdorff distance and a high recognition performance as the Gabor wavelets. Experiments based on the Yale database. AR database and ORL database show that our proposed algorithm can achieve recognition rates of 85.3%. 95.9%. and 85.7%. respectively.
Efficient likelihood Bayesian constrained local model

The constrained local model (CLM) proposes a paradigm that the locations of a set of local landmark detectors are constrained to lie in a subspace, spanned by a shape point distribution model (PDM). Fitting the model to an object involves two steps. A response map, which represents the likelihood of locations for a landmark, is first computed for each landmark using local-texture detectors. Then, an optimal PDM is determined by jointly maximizing all the response maps simultaneously, with a global-shape constraint. This global optimization can be considered a Bayesian inference problem, where the posterior distribution of the shape parameters, as well as the pose parameters, can be inferred using maximum a posteriori (MAP). In this paper, based on the CLM model, we present a novel CLM variant, which employs random-forest regressors to estimate the location of each landmark, as a likelihood term, efficiently. This novel CLM framework is called efficient likelihood Bayesian constrained local model (elBCLM). Furthermore, in each stage of the regressors, the PDM local non-rigid parameters, i.e. the shape parameters, of the previous stage can work as shape clues for training the regressors for the current stage. To further improve the efficiency, we also propose a feature-switching scheme used in the cascaded framework. Experimental results on benchmark datasets show our approach achieves about 3 to 5 times speed-up, when compared with the existing CLM models, and improves by around 10% on fitting accuracy, when compared with the other regression-based models.
Face recognition based on illumination restoration

Variations in lighting conditions make face recognition an even more challenging and difficult task. In this paper, a novel approach is proposed to handle the illumination problem. Our method can restore a face image captured under arbitrary lighting conditions to one with frontal illumination by using a ratio-image and an iterative algorithm. The restored images with frontal illumination are used for face recognition by means of PCA. Experimental results demonstrate that our method can achieve a higher recognition rate, based on the Yale B and Yale database. Moreover, our algorithm has several advantages over other previous algorithms: (1) it does not need to estimate the face surface normals and the light source directions; (2) it does not need many images captured under different lighting conditions for each person, nor a set of bootstrap images that includes many images with different illuminations; and (3) it does not need to detect accurate positions of some facial feature points and to warp the image for alignment, etc.
An efficient illumination compensation scheme for face recognition

This paper proposes a novel illumination compensation algorithm, which can compensate for the uneven illuminations on human faces and reconstruct face images of normal lighting conditions. A simple yet effective local contrast enhancement method, namely block-based histogram equalization (BHE), is first proposed. The resulting image processed by BHE is then compared with the original face image processed using histogram equalization (HE) to estimate the lighting category. Based on the category identified, a corresponding lighting compensation model is used to reconstruct an image that would visually be under normal illumination. In order to eliminate the influence of uneven illumination while retaining the shape information, a 2D face shape model is used. Experimental results show that, with the use of principal component analysis (PCA) for face recognition, the recognition rate can be improved by 53.3% to 56,1% when our proposed algorithm for lighting compensation is used.
A fast fractal image coding based on kick-out and zero contrast conditions

A fast algorithm for fractal image coding based on a single kick-out condition and the zero contrast prediction is proposed in this paper. The single kick-out condition can avoid a large number of range-domain block matches when finding the best matched domain block. An efficient method for zero contrast prediction is also proposed, which can determine whether the contrast factor for a domain block is zero or not, and compute the corresponding difference between the range block and the transformed domain block efficiently and exactly. The proposed algorithm can achieve the same reconstructed image quality as the exhaustive search, and can greatly reduce the required computation or runtime. In addition, this algorithm does not need any pre-processing step or additional memory for its implementation, and can combine with other fast fractal algorithms to further improve the speed. Experimental results show that the runtime is reduced by about 50% of that of the exhaustive search method. When combined with the DCT Inner Product algorithm, the required runtime for the algorithm can be further reduced by about 50%. The proposed algorithm was also compared to two other fast fractal algorithms. Experimental results also show that our algorithm achieves a better efficiency and requires a much smaller amount of memory for implementation.
An effieient algorithm for fractal image coding using kick-out and zero contrast conditions

In this paper, we propose a fast algorithm for fractal image coding based on a single kick-out condition and zero contrast prediction. The single kick-out condition can eliminate lots of unmatched domain blocks in the early encoding phase. An efficient method based on zero contrast prediction is also proposed, which can determine whether the contrast factor for a domain block is zero or not and compute the corresponding difference between the range block and the transformed domain block efficiently and exactly. The proposed algorithm can achieve the same reconstructed image quality as the exhaustive search, and can greatly reduce the required computational complexity. In addition, this algorithm does not need any preprocessing steps and additional memory for its implementation, and can combine with other fast fractal algorithms to further improve the speed. Experimental results show that the runtime is reduced by about 50% when compared to the exhaustive search method. The runtime can be reduced by about 75% when our algorithm is combined with the DCT inner product algorithm.
The OUC-vision large-scale underwater image database

In this paper, a large-scale underwater image database for underwater salient object detection or saliency detection is presented in detail. This database is called the OUC-VISION underwater image database, which contains 4400 underwater images of 220 individual objects. Each object is captured with four pose variations (the frontal-, the opposite-, the left-, and the right-views of each underwater object) and five spatial locations (the underwater object is located at the top-left corner, the top-right corner, the center, the bottom-left corner, and the bottom-right corner) to obtain 20 images. Meanwhile, this publicly available OUC-VISION database also provides relevant industrial fields, and academic researchers with underwater images under different sources of variations, especially pose, spatial location, illumination, turbidity of water, etc. Ground-truth information is also manually labelled for this database. The OUC-VISION database can not only be widely used to assess and evaluate the performance of the state-of-the-art salient-object detection and saliency-detection algorithms for general images, but also will particularly benefit the development of underwater vision technology in the future.
An efficient algorithm for the extraction of a Euclidean skeleton

The skeleton is essential for general shape representation but the discrete representation of an image presents a lot of problems that may influence the process of skeleton extraction. Some of the methods are memory-intensive and computationally intensive, and require a complex data structure. In this paper, we propose a fast, efficient and accurate skeletonization method for the extraction of a well-connected Euclidean skeleton based on a signed sequential Euclidean distance map. A connectivity criterion that can be used to determine whether a given pixel inside an object is a skeleton point is proposed. The criterion is based on a set of points along the object boundary, which are the nearest contour points to the pixel under consideration and its 8 neighbors. The extracted skeleton is of single-pixel width without requiring a linking algorithm or iteration process. Experiments show that the runtime of our algorithm is faster than. those of using the distance transformation and is linearly proportional to the number of pixels of an image.
Region-based feature fusion for facial-expression recognition

In this paper, we propose a feature-fusion method based on Canonical Correlation Analysis (CCA) for facial-expression recognition. In our proposed method, features from the eye and the mouth windows are extracted separately, which are correlated with each other in representing a facial expression. For each of the windows, two effective features, namely the Local Phase Quantization (LPQ) and the Pyramid of Histogram of Oriented Gradients (PHOG) descriptors, are employed to form low-level representations of the corresponding windows. The features are then represented in a coherent subspace by using CCA in order to maximize the correlation. In our experiments, the Extended Cohn-Kanade dataset is used; its face images span seven different emotions, namely anger, contempt, disgust, fear, happiness, sadness, and surprise. Experiment results show that our method can achieve excellent accuracy for facial-expression recognition.
A spectral feature based approach for face recognition with one training sample

In this paper, a novel spectral feature image-based 2DLDA (two-dimensional linear discriminant analysis) ensemble algorithm is proposed for face recognition with one sample image per person. In our algorithm, multi-resolution spectral feature images are constructed to represent the face images. The proposed method is inspired by our finding that, among these spectral feature images, features extracted from some orientations and scales using 2DLDA are not sensitive to variations of illumination and expression. In order to maintain the positive characteristics of these filters and to make correct category assignments, the strategy of classifier committee learning (CCL) is designed to combine the results obtained from different spectral feature images. Experimental results on the standard databases demonstrate the feasibility and efficiency of the proposed method.
Channel-adaptive error protection for streaming stored MPEG-4 FGS over error-prone environments

In this paper, we investigate an adaptive channel error-protection scheme for streaming stored MPEG-4 fine granular scalability (FGS) bitstreams over error-prone environments. The human eye is sensitive to the variations in reconstructed image quality. Therefore, instead of using only the minimal average frame distortion as the optimization criterion, we propose a rate-distortion (R-D)-based bit-allocation method to determine the source coding rate and the channel coding rate under a given channel condition so as to minimize both the average image distortion and quality variation. Based on our proposed piecewise model of the MPEG-4 FGS enhancement layer, a balance between the average frame quality and quality variation will be obtained. Experimental results show that, compared with other existing methods, our proposed method can achieve less quality variation and comparable average frame distortion under various channel conditions.
An Improved Method for Locating and Extracting the Eye in Human Face Images

In this paper, the head boundary is first located in a head-and-shoulders image. The approximate positions of the eyes are estimated by means of average anthropometric measures. Corners, the salient features of the eyes, are detected. The corner detection scheme introduced in this paper can provide accurate information about the corners. The shape of the eye is then extracted by a new scheme, based on snakes and on the deformable template. This new representation is in a form similar to snakes, but the energy functional to be minimised is based on some new energy terms and those terms used by the deformable template. A fast algorithm based on the greedy algorithm for active contour modelling is presented for the optimisation step. Experiments show that the execution time required for the whole procedure to extract the eye features is less than one second on a Sun workstation.
Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image

In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable. However, this method does not consider the structural characteristics of the face images, and it is difficult to determine which nonlinear mapping is more effective for face recognition. In this paper, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical property of the input features, but also adopts an eigenmask to emphasize those important facial feature points. Therefore, after this mapping, the transformed features have a higher discriminating power, and the relative importance of the features adapts to the spatial importance of the face images. This new nonlinear mapping is combined with the conventional kernel PCA to be called "doubly" nonlinear mapping kernel PCA. The proposed algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with fractional power polynomial models. Experiments show that consistent and promising results are obtained
Shivering greedy snakes, gradient-guided in wavelet domain

The convergence speed of snakes is increased with the switching step size of local minima search and they are incorporated with the local gradient information in the wavelet domain to provide extra speed of convergence to their final contours. This is important in model based compression applications using wavelet decomposition. The directional gradient information is extracted from the subbands provided for the three directions at each stage of the decomposition. The advantages of this approach include, increased speed of convergence due to the switching step size of search and due to the decreased number of pixels to be searched and decreased burden of calculations for pre-processing due to the decreased size of subbands and due to the decreased dimension of the Gaussian filters in the pre-processing stage.
Kernel PCA with doubly nonlinear mapping for face recognition

In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping kernel PCA is devised to perform feature transformation and face recognition. Our algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with fractional power polynomial (FPP) models. Experiments show that consistent and promising results are obtained.
Can ambiguous words be helpful in image-understanding systems?

A semantic gap always decreases the performance of the mapping for image-to-word, which is an important task in image understanding. Even efficient learning algorithms cannot solve this problem because: (1) of a lack of coincidence between the low-level features extracted from the visual data and the high-level information translated by human, and (2) an ambiguous word may lead to a wrong interpretation between low-level and high-level information. This paper introduces a discriminative model with a ranking function that optimizes the cost between the target word and the corresponding images, while simultaneously discovering the disambiguated senses of those words that are optimal for supervised tasks. Experiments were conducted using two datasets, and results show quite a promising result when compared with existing methods.
An efficient local-structure-based face-hallucination method

In this paper, we propose a novel patch-based face-hallucination algorithm, which is based on the local structure kernels established via the relation between interpolated low-resolution (LR) images and their corresponding high-resolution counterparts. In our algorithm, the local linear embedding (LLE) algorithm is used to extract local structures, and the kernels are then constructed based on non-overlapped patches in the interpolated LR images. The information about local structures as described by the kernels is propagated to the corresponding regions of the HR images. Sub-pixel distortions are refined by solving a constrained problem at pixel level via iterative procedures. Experimental results show that our proposed method can provide a good performance in terms of reconstruction errors and visual quality.
A hierarchical and adaptive deformable model for mouth boundary detection

An automatic algorithm to extract mouth boundaries in human face images is proposed. The algorithm is based on a hierarchical model adaptation scheme using deformable models. The knowledge about the shape of the object is used to define its initial deformable template. Each mouth boundary curve is initially formed based on three control points whose locations are found through an optimization process using a suitable cost functional. The cost functional captures the essential knowledge about the shape for perceptual organization. Two control points are the mouth corners, which are used as the initial location of the mouth after an approximate mouth window is found based on locating the head boundary. The model is hierarchically improved in the second stage of the algorithm. Each boundary curve is finely tuned using more control points. An old model is adaptively replaced by a new model only if a secondary cost is further reduced. The results show that the model adaptation technique satisfactorily enhances the mouth boundary model in an automated fashion.
An efficient and accurate algorithm for extracting a skeleton

In this paper, a non-iterative method is proposed, which is fast, efficient, and more importantly, is robust to boundary noise and rotation. Unnecessary branches and hairs can be reduced by the adjustment of the residual distance and the skeleton can be represented in a hierarchical manner. The reconstruction error can also be estimated using the residual distance. A new definition of a skeleton and the criteria of being a skeleton point are introduced. The effect of boundary noise and curved boundary are investigated and compared to other skeletonization algorithms. Finally, the reconstruction of an object using its skeleton and the associated radii of the maximal disks, is illustrated.
Rotation-invariant texture analysis using multi-resolution Slit kernels

This paper presents a set of simple Slit kernel suitable for regular and rotation-invariant texture analysis. The fast algorithm used to compute its feature is also presented. Extracting Slit features requires 11 to 17% of arithmetic operations when compared to that for Gabor features. Experimental results for the classification of rotated texture images indicate that Slit kernels perform as well as, or sometimes better than, Gabor ones. This makes Slit suitable for applications where computational speed is important.