nils-herrmann/clean_scirepeval huggingface dataset:
Towards parameter-less support vector machines

Support vector machines (SVMs) are a widely-used machine learning technique, but they suffer from a significant drawback of high time and memory training complexity, which should be endured especially in big data problems. SVMs incorporate kernel functions - it involves selecting the kernel and induces an additional computational effort. In this paper, we address these issues and propose an SVM framework that automatically determines the kernel and selects data to train SVMs. It embodies the neuro-fuzzy system for creating the kernel along with the memetic algorithm to select training samples. Extensive experiments indicate that our approach enables obtaining high classification scores.
Competitive image colorization

This paper presents a new method for image colorization based on manually added scribbles. We determine color propagation paths in the image by minimizing the geodesic distance from the scribbles using Dijkstra algorithm. After that, chrominance blending is performed to colorize the image. Our contribution lies in proposing the competitive approach for selecting an appropriate type of the path cost. Moreover, we introduce a modified chrominance distance calculated along each path. Benefits of these improvements are explained and justified. The experiments confirmed that the proposed algorithm yields better results compared with other well-established methods.
Adaptive Vision Studio — Educational tool for image processing learning

In this paper, we present Adaptive Vision Studio (AVS) - a novel tool for creating image processing and analysis algorithms. AVS has been applied in post-graduate computer vision course for students of Automatic Control and Biotechnology at Silesian University of Technology. This software is a powerful environment with ready-for-use image analysis filters for computer vision experts as well as for engineers, who are beginners in this field. AVS has been published as a freeware version for noncommercial and educational purposes recommended for students and engineers, who want to learn how to develop complex image processing algorithms. Lite version of AVS is freely available at https://adaptive-vision.com.
Parallel Hand Shape Classification

This paper introduces a new parallel algorithm (PA) for fast hand shape classification. This problem is challenging as a hand is characterized by a high number of degrees of freedom. Our objective is to design and implement a robust algorithm suitable for real-time applications. We show how the analysis time can be decreased, together with the increase of the classification accuracy, by the means of parallelization. Also, we propose to combine the shape contexts approach with the appearance-based techniques to increase the efficacy of the PA. An extensive experimental study confirms the effectiveness of the proposed PA compared with other state-of-the-art methods.
Gaze direction estimation from static images

This study presents a novel multilevel algorithm for gaze direction recognition from static images. Proposed solution consists of three stages: (i) eye pupil localization using a multistage ellipse detector combined with a support vector machines verifier, (ii) eye bounding box localization calculated using a hybrid projection function and (iii) gaze direction classification using support vector machines and random forests. The proposed method has been tested on Eye-Chimera database with very promising results. Extensive tests show that eye bounding box localization allows us to achieve highly accurate results both in terms of eye location and gaze direction classification.
Fast propagation-based skin regions segmentation in color images

This paper introduces a new method for skin regions segmentation which consists in spatial analysis of skin probability maps obtained using pixel-wise detectors. There are a number of methods which use various techniques of skin color modeling to classify every individual pixel or transform input color images into skin probability maps, but their performance is limited due to high variance and low specificity of the skin color. Detection precision can be enhanced based on spatial analysis of skin pixels, however this direction has been little explored so far. Our contribution lies in using the distance transform for propagating the “skinness” across the image in a combined domain of luminance, hue and skin probability. In the paper we explain theoretical advantages of the proposed method over alternative skin detectors that also perform spatial analysis. Finally, we present results of an extensive experimental study which clearly indicate high competitiveness of the proposed method and its relevance to gesture recognition.
Extracting gender discriminating features from facial needle-maps

In this paper, we show how to extract gender discriminating features from 2.5D facial needle-maps. The standard eigenspace analysis method for non-Euclidean data is principal geodesic analysis (PGA). Based on PGA, we propose a novel supervised weighted PGA method which incorporates local weights into standard PGA to improve gender discriminating capability of the extracted features. The weight map is iteratively optimized from the labeled data, which is different from other gender relevant weights used in the literature . Experimental results illustrate the effectiveness of this method and its successful application to gender classification.
Adaptive memetic algorithm for the job shop scheduling problem

Solving the job shop scheduling problem (JSP) is a vital research topic due to its wide practical applicability. It is an NP-hard discrete optimization problem which was transformed into a plethora of variants reflecting other real-life scenarios. In this paper, we propose an adaptive memetic algorithm (MA) to solve the JSP. It consists in determining a schedule for completing jobs (divided into operations) on a set of available machines. MAs, which are the hybrids of genetic algorithms and refinement procedures, were shown to be very efficient in tackling complex problems in many fields of science and engineering. In the proposed algorithm (AMXMA), a number of children are generated for each pair of parents to exploit them intensively. We keep three solution representations (if necessary) within each individual to avoid the necessity of transforming one representation into another required by various crossover operators. In addition, we introduce the adaptive selection scheme which is dynamically controlled on the fly to effectively balance the exploration and exploitation of the search space. An extensive experimental study performed on a widely-used benchmark set of problems with various sizes shows that AMXMA is extremely efficient in terms of the computation time and allows for fast convergence to very high-quality solutions. We show that AMXMA is highly competitive compared with other state-of-the-art algorithms.
Towards robust SVM training from weakly labeled large data sets

Learning from large data sets that contain samples of unknown or incorrect labels becomes increasingly important. Such problems are inherent to many big data scenarios, hence there is a need for developing robust generic approaches to learning from difficult data. In this paper, we propose a new memetic algorithm that evolves samples and labels to select a training set for support vector machines from large, weakly-labeled sets. Our extensive experimental study confirmed that the new method presents high robustness against weakly-labeled data and outperforms other state-of-the-art algorithms.
Adaptive feature extraction for blurred face images in facial expression recognition

In real world facial expression recognition, blurred face images could hamper achieving high performance due to the lack of distinct edges and textures. In this paper, we propose a new feature extraction method that is robust to blurred face images for facial expression recognition. In the proposed method, the facial feature is extracted adaptively depending on the image sharpness, aiming to achieve robustness against blurred face images. Experimental results on blurred face images demonstrate that the proposed method outperforms the exiting feature extraction method.
Improvement of subtle microcalcifications detection in DBT slices

In this paper, a novel microcalcification detection in Digital Breast Tomosynthesis (DBT) is proposed, which allows detecting subtle microcalcifications that have relatively low contrast in DBT slices. 3D Foveal segmentation is employed for volume of interest (VOI) detection using adaptive thresholding scheme by local volume contrast. Furthermore, in order to conserve structural characteristics of clustered microcalcifications, features from VOI maximum ray-tracing images with three orthogonal directions are extracted. Extensive and comparative experiments were carried out to demonstrate the effectiveness of the proposed 3D Foveal segmentation and the feature extraction method on clinical DBT cases. The results showed that the proposed microcalcification detection achieves a sensitivity of 98% at 0.5 FP per volume.
Improving image tag recommendation using favorite image context

Tag recommendation allows mitigating the amount of user effort needed to annotate images. Assuming that favorite images and their associated tags are indicative of the visual and topical interests of users, this paper proposes a personalized image tag recommendation technique that makes use of favorite image context. Specifically, to recommend tags for a newly uploaded image, we propose to take advantage of the tags assigned to favorite images of the user who uploaded the image, fusing tag statistics and visual similarity. Experimental results obtained for images and tags retrieved from Flickr compare the use of favorite image context to the use of personal and collective context for the purpose of tag recommendation, showing that the use of favorite image context is promising.
Local color vector binary pattern for face recognition

This paper proposes local color vector binary pattern (LCVBP) as a new color based face descriptor for face recognition (FR). In the proposed LCVBP, color pixel pattern of different spectral images is extracted as discriminative information. Further, to maximize a complementary effect taken from different color channels, color angle pattern between a pair of different spectral images is incorporated into the creation of LCVBP. Experimental results on three public databases (CMU-PIE, XM2VTSDB, and Color FERET) show that the proposed color texture feature is able to significantly improve FR performance, compared to the conventional grayscale texture features including Gabor wavelet and Local Binary Pattern (LBP). In particular, the proposed LCVBP is able to provide excellent FR performance for face images taken under severe illumination variation.
A selective video encryption for the region of interest in scalable video coding

In this paper, we propose an encryption method to protect the region of interest (ROI) which is standard element in the scalable video coding (SVC). The proposed video encryption method provides secured SVC contents with the selective encryption scheme. In order to confine the encryption into the ROI, three encoding schemes are also proposed, which include the constrained motion estimation, exceptional handlings for the half-pel interpolation and Intra_BL mode in the boundary of a ROI. Experiments were performed to verify the proposed methods using the joint scalable video model (JSVM), and the experimental results showed that the proposed methods protect the ROI in SVC effectively .
Human action recognition using time-invariant key-trajectories describing spatio-temporal salient motion

Human action recognition (HAR) has been attracting much attention in the computer vision arena. In particular, many research efforts were dedicated for developing discriminative feature extraction methods for improving the HAR performance. Among them, trajectory-based features have shown state-of-the-art performance. However, the time-variance of trajectory-based feature and the large number of indistinctive trajectories describing the human action can limit their performance. In this paper, we propose extracting human action features from a distinctive subset of trajectories, namely key-trajectories. Moreover, the key-trajectories are extracted in a time-invariant manner, so that they are able to represent human action regardless of the time at which the action occurs. With publically available and challenging datasets, comparative experiments have been conducted. Results show that the proposed key-trajectory feature extraction improves the HAR performance thanks to their distinctive and time-invariant characteristics.
Collaborative facial color feature learning of multiple color spaces for face recognition

Facial color is known as playing an important role in face recognition. Color face recognition has been investigated in the last decade. Recently, deep learning has attracted considerable attention due to their high performance in face recognition. The importance of the color in a deep learning framework is not fully investigated yet. In this paper, we have conducted experiments to investigate the effectiveness of facial color in face recognition with deep learning. Through experimental results, we have demonstrated that facial color is helpful for enhancing the recognition performance in deep learning and color space selection is crucial to achieve high performance. Moreover, by fusing features from multiple color spaces, the face recognition accuracy has been considerably improved.
Inter-view consistent hole filling in view extrapolation for multi-view image generation

This paper proposes a new inter-view consistent hole filling method in view extrapolation for multi-view image generation. In stereopsis, inter-view consistency regarding structure, color, and luminance is one of the crucial factors that affect the overall viewing quality of three-dimensional image contents. In particular, the inter-view inconsistency could induce visual stress on the human visual system. To ensure the inter-view consistency, the proposed method suggests a hole filling method in an order from the nearest to farthest view to the reference view by propagating the filled color information in the preceding view. In addition, a novel depth map filling method is incorporated to achieve the inter-view consistency. Experimental results show that the proposed method significantly improves the inter-view consistency for multiview images and depth maps, compared to those of previous methods.
Modality conversion for QoS management in universal multimedia access

Modality conversion currently emerges as an important issue in universal multimedia access. The decision on modality conversion is affected by various factors, such as terminal capability, user preferences, surrounding environment, etc. Here, modality conversion under the constraint of connection bitrate is considered. Intuitively, when content scaling cannot provide the acceptable QoS, modality conversion may be a good choice to deliver an appropriate quality. From the QoS point of view, two important questions in modality conversion are: 'at what resource constraint point should a change of modality occur?' and 'what is the destination modality?' That is, knowing the conversion boundaries between modalities is crucial for a seamless modality conversion. In this paper, a systematic approach to help answer these questions is presented.
Visual contents adaptation for color vision deficiency

In this paper, we propose methods to adapt colors on the visual content for people with color vision deficiency. The proposed adaptation consists of two parts: adaptations for dichromat and anomalous trichromat. The adaptation for dichromats aims to give them better color information, while the adaptation for anomalous trichromats aims to give them original color. To verify the proposed methods, we used both quantitative and qualitative measurements. Experimental results showed that the proposed adaptation enhanced color information readability of the people with color vision deficiency.
Region based stellate features for classification of mammographic spiculated lesions in computer-aided detection

In this paper, new region-based stellate features have been developed for correctly differentiating spiculated malignant lesions from normal tissues in mammography. The purpose of using proposed features is to reduce the number of false positive that are produced during the detection of suspicious regions in computeraided detection (CAD). It has been well-known that one particularly important characteristic of spiculated lesions is that they have usually radiating patterns of linear spicules. Based on the aforementioned observation, we propose effective region-based stellate features, designed for well representing the stellate pattern information within a given region-of-interest (ROI). In particular, the proposed features are calculated using statistical information of the stellate patterns within local regions of a given ROI. The effectiveness of our stellate features has been successfully tested on two public mammogram databases (DBs).
Sub-sampled dictionaries for coarse-to-fine sparse representation-based human action recognition

Automatic human action recognition is a core functionality of systems for video surveillance and human-object interaction. However, the diverse nature of human actions and the noisy nature of most video content make it difficult to achieve effective human action recognition. To overcome the aforementioned problems, Sparse Representation (SR) has recently attracted substantial research attention. However, although SR-based approaches have proven to be reasonably effective, the computational complexity of the testing stage prohibits their usage by applications requiring support for real-time operation and a vast number of human action classes. In this paper, we propose a novel method for human action recognition, leveraging coarse-to-fine sparse representations that have been obtained through dictionary sub-sampling. Comparative experimental results obtained for the UCF50 dataset demonstrate that the proposed method is able to achieve efficient human action recognition, at no substantial loss in recognition accuracy.
Color Local Texture Features for Color Face Recognition

This paper proposes new color local texture features, i.e., color local Gabor wavelets (CLGWs) and color local binary pattern (CLBP), for the purpose of face recognition (FR). The proposed color local texture features are able to exploit the discriminative information derived from spatiochromatic texture patterns of different spectral channels within a certain local face region. Furthermore, in order to maximize a complementary effect taken by using both color and texture information, the opponent color texture features that capture the texture patterns of spatial interactions between spectral channels are also incorporated into the generation of CLGW and CLBP. In addition, to perform the final classification, multiple color local texture features (each corresponding to the associated color band) are combined within a feature-level fusion framework. Extensive and comparative experiments have been conducted to evaluate our color local texture features for FR on five public face databases, i.e., CMU-PIE, Color FERET, XM2VTSDB, SCface, and FRGC 2.0. Experimental results show that FR approaches using color local texture features impressively yield better recognition rates than FR approaches using only color or texture information. Particularly, compared with grayscale texture features, the proposed color local texture features are able to provide excellent recognition rates for face images taken under severe variation in illumination, as well as for small- (low-) resolution face images. In addition, the feasibility of our color local texture features has been successfully demonstrated by making comparisons with other state-of-the-art color FR methods.
User description and media adaptation for low vision accessibility

Ethical and legal requirements have made accessibility a cruciat feature for any information systems. However, the current accessible technologies are just suitable for either mild impairments or total impairments. This paper deals with visual content adaptation, in the context of the MPEG-21 standard, to help low vision users have better accessibility to the contents. The proposed approach includes descriptions for the low vision impairment and content adaptation techniques that meet the characteristics of users. The major advantages o f the proposed approach include I ) support of a wide range of low vision conditions, 2) customized content adaptation to specific characteristics of each user.
Color component feature selection in feature-level fusion based color face recognition

In this paper, we propose a new color face recognition (FR) method which effectively employs feature selection algorithm in order to find the set of optimal color components (from various color models) for FR purpose. The proposed FR method is also designed to improve FR accuracy by combining the selected color components at the feature level. The effectiveness of the proposed color FR method has been successfully demonstrated using two public CMU-PIE and Color FERET face databases (DB). In our comparative experiments, traditional grayscale-based FR, previous color-based FR, and popular local binary pattern (LBP) based FR methods were compared with the proposed method. Experimental results show that our color FR method performs better than the aforementioned three different FR approaches. In particular, the proposed method can achieve 7.81% and 18.57% improvement in FR performance on the CMU-PIE and Color FERET DB, respectively, compared to representative color-based FR solutions previously developed.
Multi-Scale Facial Scanning via Spatial Lstm for Latent Facial Feature Representation

In the past few decades, automatic face recognition has been an important vision task. In this paper, we exploit the spatial relationships of facial local regions by using a novel deep network. In the proposed method, face is spatially scanned with spatial long short-term memory (LSTM) to encode the spatial correlation of facial regions. Moreover, with facial regions of various scales, the complementary information of the multi-scale facial features is encoded. Experimental results on public database showed that the proposed method outperformed the conventional methods by improving the face recognition accuracy under illumination variation.
Boosting chromatic information for face recognition

In this paper, chromatic information is integrated with an Adaboost learner to address non linearities in face patterns and illumination variations in training databases for face recognition (FR). An LDA based learner is boosted and the integrated framework is tested on a large database of images having severe pose and illumination variations. The increased dimensionality of color induces a small sample size problem when used with an LDA based learner. The integrated framework is tested on a number of learning scenarios in order to examine this effect. Experimental results show that integrating color into the boosting framework produces a high performing FR system for a range of learning scenarios.