nils-herrmann/clean_scirepeval huggingface dataset:
Human face recognition using a spatially weighted Hausdorff distance

The edge map of a facial image contains abundant information about its shape and structure, which is useful for face recognition. To compare edge images, Hausdroff distance is an efficient measure that can determine the degree of their resemblance, and does not require a knowledge of correspondence among those points in the two edge maps. In this paper, a new modified Hausdorff distance measure is proposed, which has a better noise immunity capability and better discriminant power. As the different facial regions have different relative importance for face recognition, the modified Hausdorff distance is weighted according to a weighted function derived from the spatial. Information of the human face; hence crucial regions are emphasized for face identification. Experimental results show that the distance measure can achieve recognition rates of 82%, 93%, and 97% for the first, the first three, and the first five likely matched faces, respectively.
Feature subset selection for efficient AdaBoost training

Working with a very large feature set is a challenge in the current machine learning research. In this paper, we address the feature-selection problem in the context of training AdaBoost classifiers. The AdaBoost algorithm embeds a feature selection mechanism based on training a classifier for each feature. Learning the single-feature classifiers is the most time consuming part of AdaBoost training, especially when large number of features are available. To solve this problem, we generate a working feature subset using a novel feature subset selection method based on the partial least square regression, and then train and select from this feature subset. The partial least square method is capable of selecting high-dimensional and highly redundant features. The experiments show that the proposed PLS-based feature-selection method generates sensible feature subsets for AdaBoost in a very efficient way.
Simultaneous Hallucination and Recognition of Low-Resolution Faces Based on Singular Value Decomposition

In video surveillance, the captured face images are usually of low resolution (LR). Thus, a framework based on singular value decomposition (SVD) for performing both face hallucination and recognition simultaneously is proposed in this paper. Conventionally, LR face recognition is carried out by super-resolving the LR input face first, and then performing face recognition to identify the input face. By considering face hallucination and recognition simultaneously, the accuracy of both the hallucination and the recognition can be improved. In this paper, singular values are first proved to be effective for representing face images, and the singular values of a face image at different resolutions have approximately a linear relation. In our algorithm, each face image is represented using SVD. For each LR input face, the corresponding LR and high-resolution (HR) face-image pairs can then be selected from the face gallery. Based on these selected LR-HR pairs, the mapping functions for interpolating the two matrices in the SVD representation for the reconstruction of HR face images can be learned more accurately. Therefore, the final estimation of the high-frequency details of the HR face images will become more reliable and effective. The experimental results demonstrate that our proposed framework can achieve promising results for both face hallucination and recognition.
Maximal disk based histogram for shape retrieval

We propose a robust and efficient representation scheme for shape retrieval, which is based on the normalized maximal disks used to represent the shape of an object. The maximal disks are extracted by means of a fast skeletonization technique with a pruning algorithm. The logarithm of the radii of the normalized maximal disks is used to construct a histogram to represent the shape. The retrieval performance of this maximal disk based histogram approach is compared to other methods, including moment invariants, Zernike moments, and curvature scale-space. Experimental results show that our proposed representation scheme outperforms the other methods under affine transformation and different noise levels.
Elastic Local Reconstruction for Human Face Recognition

In this paper, a new face recognition algorithm based on a single frontal-view image for each face subject is proposed, which considers the effect of the face manifold structure. A human face is considered a combination of a sequence of local image blocks. Then an elastic local reconstruction (ELR) method is proposed to measure the similarities between the image block pairs in order to measure the difference between the two face images. Our algorithm not only benefits from the face manifold structure, in terms of being robust to various image variations, but also is computationally simple because there is no need to build the face manifold. We evaluate the performance of our proposed face recognition algorithm with the use of different databases and consistent and promising experimental results were obtained
The GVF Snake with a Minimal Path Approach

In this paper we propose a contour extraction method based on the active contour model, which uses the GVF snake to obtain the initial segments for a contour; and then a minimal path method for the refinement stage, to obtain an accurate and more robust result. By employing the minimal path method to find missing segments between pairs of nodes defined on the contour obtained by the GVF snake, our algorithm is able to detect deep concave parts of object boundary, and works well even when the snake initialization is not very good.
Partially occluded face completion and recognition

This paper proposes a spectral graph based algorithm for face image repairing, which can improve the recognition performance on occluded faces. Our algorithm is called ‘guided label-learning’, so named from graphical models, and can achieve a high-quality repairing of damaged or occluded faces. We apply our face repairing algorithm in order to produce completed faces, and then use face recognition to evaluate the performance of our algorithm. Experiment results show that, at most, a nearly 30%-increase in the recognition rate can be achieved for occluded faces with the use of our algorithm.
A unified shot boundary detection method based on linear prediction with Bayesian cost functions

The detection of edits in a video sequence is the first step for video analysis, which segments a video into its basic components. In this paper, we propose a novel and efficient approach for shot boundary detection, which can detect cuts and dissolves reliably using a uniform framework. Our approach is based on the temporal linear prediction of the frames. With frames in a video shot, a frame can be predicted from its previous frames. If the prediction error is high, a cut should happen. For dissolves, the gradual transitions can be modeled by temporal linear prediction with constant prediction coefficients. Experimental results show that our algorithm can achieve high precision even if a video contains object motion and camera motion, and is able to detect and classify cuts and dissolves in real time.
Elastic block set reconstruction for face recognition

In this paper, a novel face recognition algorithm named elastic block set reconstruction (EBSR) is proposed. In our method, the EBSR face is used to represent a set of training faces and to simulate different factors in a query image. An EBSR face is constructed by using the blocks from the training face images which best match to the blocks of the query image at the corresponding locations. The elastic local reconstruction (ELR) error is then used to evaluate how well a block pair matches, and the query image is classified based on the accumulated reconstruction error. The proposed method can effectively explore local information in the training set and deal with various conditions well. Also, the reconstruction error can be considered as a kind of dissimilarity measure, which gives a new approach to designing the training set so as to maximize robustness of recognition. Experiments show that consistent and promising results are obtained.
Color facial image denoising based on rpca and noisy pixel detection

In this paper, a novel approach for color facial-image denoising based on robust principal component analysis (RPCA)  in the L*a*b* color space and noisy pixel detection is proposed. Firstly, RPCA is employed for color facial-image recovery in the L*a*b* space. Then, the reconstructed image is used for noisy pixel detection. Finally, the denoised facial-image can be obtained. Experiments are conducted based on the AR database, where our proposed method is compared with several state-of-the-art image-denoising methods. Experimental results show that our method can achieve a better performance in terms of both quantitatively evaluation and visual quality.
Mammogram microcalcification cluster detection by locating key instances in a Multi-Instance Learning framework

A new scheme for the computer-aided diagnosis (CAD) of microcalcification clusters (MCCs) detection in a Multi-Instance Learning (MIL) framework is proposed in this paper. To achieve a satisfactory performance, our algorithm first searches for possible candidates of microcalcification clusters using the mean-shift algorithm. Then, features are extracted from the potential candidates based on a constructed graph. Finally, a multi-instance learning method which locates the key instance in each bag of features is used to classify the possible candidates. Experimental results show that our scheme can achieve a superior performance on public datasets, and the computation is efficient.
Embedded human face image coding with set partitioning in hierarchical trees

For human face images, the region of human face (ROHF) is considered to be the most important part, while the background is allowed to have degraded quality because it is considered to be less important. In this paper, a human face detection algorithm is combined with the set partitioning in hierarchical trees (SPIHT) algorithm for wavelet-based image coding. The human face detection algorithm is employed to automatically determine the ROHF in a human face image. The ROHF mask is generated in the wavelet domain. The wavelet coefficients in the ROHF mask of the LL subband are scaled to ensure that they are encoded with a higher priority. Finally the SPIHT algorithm is directly employed to encode the resulting coefficients progressively. Experimental results show that the ROHF exhibits much better quality than that of the background region at any bit rate. The encoded bitstream based on this approach is fully embedded and supports progressive transmission.
Segmentation-enhanced saliency detection model based on distance transform and center bias

Saliency detection is one of the extraordinary abilities of the human visual system (HVS), and also provides a powerful tool for predicting where humans tend to focus in the free-viewing process. In this paper, we propose a novel method for computing image saliency. At first, an image is subject to L0 smoothing to characterize its fundamental constituents while diminishing insignificant details. Distance-transform-based saliency detection is then applied to the smoothed image, to extract the general salient regions and form a rough saliency map. Next, the segmentation information generated by normalized cuts is used to improve the saliency detection performance by averaging the saliency values in each segmented block. Finally, we employ the center-bias mechanism to further improve the saliency model. The proposed method is compared with six existing saliency models, and achieves the best performance in terms of the area under the ROC curve (AUC).
Adaptive forward error correction for streaming stored MPEG-4 FGS video over wireless channel

Video streaming over wireless channel encounters many challenges due to the time-varying characteristics of wireless channels and limited network bandwidth. In this paper, we investigate an adaptive forward error correction (FEC) scheme for streaming the stored MPEG-4 FGS bit-stream from the video server to the clients over wireless channel. In this scheme, the base layer is transmitted reliably and the enhancement layer is protected with unequal error protection (UEP) scheme. Taking the fine-grained characteristics of FGS into account, we propose a decodable rate maximized criterion to optimally allocate the bit rate between the source rate and the channel coding rate and dynamically adapt to the varying network condition. Experimental results show that Compared with fixed unequal error protection (UEP) and equal error protection (EEP), our proposed method can provide a better performance.
Locating the human eye using fractal dimensions

A new method for locating eye pairs based on valley field detection and measurement of fractal dimensions is proposed. Fractal dimension is an efficient representation of the texture of facial features. Possible eye candidates to an image with a complex background are identified by valley field detection. The eye candidates are then grouped to form eye pairs if their local properties for eyes are satisfied. Two eyes are matched if they have similar roughness and orientation as represented by fractal dimensions. We propose a modified approach to estimate the fractal dimensions that are less sensitive to lighting conditions and provide information about the orientation of an image under consideration. Possible eye pairs are further verified by comparing the fractal dimensions of the eye-pair window and the corresponding face region with the respective means of the fractal dimensions of the eye-pair windows and the face regions. The means of the fractal dimensions are obtained based on a number of facial images in a database. Experiments have shown that this approach is fast and reliable. This shows that the texture of the eyes can be represented very well by fractal surfaces.
An efficient and effective hybrid pyramid kernel for un-segmented image classification

Automatic object annotation usually requires complicated segmentation to separate foreground objects from the background scene. However, the statistical content of a background scene can in fact provide resourceful valuable information for image retrieval. In this paper, we propose a new hybrid kernel that incorporates local features extracted from both dense regular grids and interest points for image classification, without requiring segmentation. Features extracted from dense regular grids can better capture information about the background scene, while interest points detected at corners and edges can better capture information about the salient objects. In our algorithm, these two local features are combined in both the spatial and the feature-space domains, and are organized into pyramid representations. From the experimental results, we observe that our algorithm achieved a 4.5% increase in performance as compared to other pyramid-representation-based methods. The proposed hybrid kernel has been proven to satisfy Mercer's condition and is particularly efficient in measuring the similarities between image features. For instance, the computational complexity of the proposed hybrid kernel is proportional to the number of features.
Multiple-Kernel, Multiple-Instance Similarity Features for Efficient Visual Object Detection

We propose to use the similarity between the sample instance and a number of exemplars as features in visual object detection. Concepts from multiple-kernel learning and multiple-instance learning are incorporated into our scheme at the feature level by properly calculating the similarity. The similarity between two instances can be measured by various metrics and by using the information from various sources, which mimics the use of multiple kernels for kernel machines. Pooling of the similarity values from multiple instances of an object part is introduced to cope with alignment inaccuracy between object instances. To deal with the high dimensionality of the multiple-kernel multiple-instance similarity feature, we propose a forward feature-selection technique and a coarse-to-fine learning scheme to find a set of good exemplars, hence we can produce an efficient classifier while maintaining a good performance. Both the feature and the learning technique have interesting properties. We demonstrate the performance of our method using both synthetic data and real-world visual object detection data sets.
Efficient face recognition with a large database

Face recognition has a wide range of applications, and most of the current face recognition algorithms can achieve a high level of accuracy. However, when the face database is very large, the amount of computation required to search the faces in that database becomes an important concern. In this paper, we propose to use a number of vantage objects to form an efficient indexing structure for searching a huge database. These vantage objects are constructed using the discriminative features extracted from Gabor wavelets. The training faces in the database are ranked either in ascending or descending order with reference to each of the vantage objects, and hence each vantage object can form one or more ranked lists. With a query face, it is ranked with reference to each vantage object, and is positioned in each of the ranked lists accordingly. Then, the neighboring training faces to the query face in the respective ranked lists are selected to form a much smaller database, which is called a condensed database. Experimental results show that, with a database of more than 2000 distinct faces, the probabilities of a query face being selected in a condensed database of 36%, 26%, 11%, and 6% of the original database size are 99%, 98%, 95%, and 90%, respectively. To search a face in the much smaller condensed database, a more computational and accurate recognition algorithm can then be adopted.
Feature-aging for age-invariant face recognition

Age-invariant face recognition has attracted some recent attention. In real applications, the age progression of those face images, stored in a face database for recognition and identification purposes, should also be considered, so as to achieve a higher accuracy level. In this paper, we propose a method to predict the aging of facial features so as to alleviate the effect of age progression on face recognition. The original facial feature and the aged facial feature of a face image should be correlated, so they are fused by using canonical correlation analysis to form a coherent feature for face recognition. The performance of our proposed approach is evaluated based on the FGNet database, and compared to some existing face recognition algorithms. Experiment results show that our proposed method can achieve a superior performance, when the query and probe face images have a large age difference.
An analysis on the realisation of two-dimensional image moments

Presents an analysis and evaluation of the original approach and other realization techniques for computing two-dimensional image moments. In the original approach pixels are divided into blocks and a simple table look-up technique is used for the major part of the realization. The 80386 assembly language, the C-language, and a T800 transputer system have been used to evaluate the new approach and other techniques. It is found that the current approach gives superior performance over other algorithms. In particular, the current approach is 1 to 10 times faster than other approaches for serial realization, whereas a speed-up of 6 to 7 is achievable for parallel realization using seven transputers connected in the tree topology. In some cases, superlinear speed-up is also observed, which shows the superior quality of the new approach.<<ETX>>
A new approach using modified Hausdorff distances with eigenface for human face recognition

Hausdorff distance is an efficient measure of the similarity of two point sets. In this paper, we propose two new spatially weighted Hausdorff distance measures for human face recognition, namely, spatially eigen-weighted Hausdorff distance (SEWHD) and spatially eigen-weighted 'doubly' Hausdorff distance (SEW2HD). These new Hausdorff distances incorporate the information about the location of important facial features so that distances at those regions will be emphasized. The weighting function used in the Hausdorff distance measure is based on an eigenface, which has a large value at locations of important facial features and can reflect the face structure more effectively. Experimental results based on a combination of the ORL, MIT, and Yale face databases show that SEW2HD can achieve recognition rates of 83%, 90% and 92% for the first one, the first three and the first five likely matched faces, respectively, while the corresponding recognition rates of SEWHD are 80%, 83% and 88%, respectively.
An efficient color compensation scheme for skin color segmentation

Skin color is a useful means for human face detection. In this paper, we propose an efficient color compensation method for skin color segmentation under varying lighting conditions. One of the major problems of using skin color is that a face region may not be detected under poor or uneven lighting conditions. Our approach considers the distribution of the color components of skin pixels and the color response of capturing machines under different illuminations. Experimental results show that this algorithm can improve the performance of face segmentation under poor or strong lighting conditions.
Color correction via robust reference selection and recovery using a low-rank matrix model

In this paper, we propose a method that can handle the color correction of a large collection of photographs simultaneously and automatically via robust reference selection. The method does not use any particular model to handle the errors on the photographs, but corrects all kinds of errors caused by changes of viewpoint, large illumination variations, gross pixel corruptions, and partial occlusions under a low-rank matrix model. Furthermore, our method uses the image pixel values directly in vector form, which preserves the spatial information, to obtain the matrix for color correction, unlike other statistics-based image-representation methods such as color histograms. Experiments verify that our method can achieve consistent and promising results on uncontrolled real photographs acquired from the Internet.
Constructing a hierarchical tree for image annotation

Image annotation is always an easy task for humans but a tough task for machines. Inspired by human's thinking mode, there is an assumption that the computer has double systems. Each of the systems can handle the task individually and in parallel. In this paper, we introduce a new hierarchical model for image annotation, based on constructing a novel, hierarchical tree, which consists of exploring the relationships between the labels and the features used, and dividing labels into several hierarchies for efficient and accurate labeling.
Mesh Simplification Algorithm Based on Quadrangle Collapse

Many applications of Virtual Reality require 3D models. In this paper, quadrangles in the triangle mesh are defined. We put forward a quadrangle collapse based mesh simplification algorithm which can rapidly produce high approximations of 3D models. The algorithm uses iterative collapse of quadrangle to simple models and maintains surface error approximations using quadric matrices. Three vertices and six faces are collapsed in one iteration, thereby the algorithm can rapidly generate high-quality approximations of polygonal models with minimal times of collapses than Garland's and Pan's algorithms. The experiment results demonstrate the efficiency of the new algorithm.