nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of HEp-2 images using MD-LBP and MAD-bagging

Indirect immunofluorescence imaging is employed to identify antinuclear antibodies in HEp-2 cells which founds the basis for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. Six categories of HEp-2 cells are generally considered, namely homogeneous, fine speckled, coarse speckled, nucleolar, cyto-plasmic, and centromere cells. Typically, this categorisation is performed manually by an expert and is hence both time consuming and subjective. In this paper, we present a method for automatically classifiying HEp-2 cells using texture information in conjunction with a suitable classification system. In particular, we extract multidimensional local binary pattern (MD-LBP) texture features to characterise the cell area. These then form the input for a classification stage, for which we employ a margin distribution based bagging pruning (MAD-Bagging) classifier ensemble. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to give excellent performance, superior to all algorithms that were entered in the competition.
A comparison of two methods for retrieval of medical images in the compressed domain

Retrieval of images based on features derived directly from the images provides a useful way of accessing medical imagery. While most of these techniques operate in the pixel domain, a more efficient approach is to perform retrieval directly in the compressed domain of images. In this paper we look at two different methods for such compressed-domain retrieval of medical images. Both algorithms are based on wavelet image compression such as the one used in the JPEG2000 standard. We compare the effectiveness of the two techniques based on a database of 400 medical infrared images.
Improved dominant local binary pattern texture features

Texture features are important in many computer vision applications. LBP is a simple yet powerful texture descriptor that is based on the concept of local binary patterns which describe the relationships of pixels to their local neighbourhood. These relationships are encoded in binary form, and the resulting patterns are then typically used to build histograms over an image or image region. It is observed that only relatively few of these patterns occur frequently in images. Dominant LBP (D-LBP) is a variant of LBP based on these dominant LBP patterns. D-LBP re-arranges the histogram bins in descending order of frequency and then selects the first few dominant patterns as texture features. By doing so, however, it discards the information of which patterns are selected. In this paper, we propose an improved Dominant LBP algorithm that preserves the pattern information and show it, based on an extensive set of experiments on several Outex benchmark datasets, to outperform D-LBP for texture classification.
Stable and reliable predictive accuracy of robust weighted slope one under profile injection attacks

The Slope One class of algorithms have been shown to lead, although being relatively simple, to accuracies that are very close to the more commonly utilised memory-based Collaborative Filtering (CF) algorithms. In addition, this class of algorithms is highly scalable in comparison to memory-based algorithms. A recently observed phenomenon are profile injection attacks on recommendation algorithms that tend to increase (push attack) or decrease (nuke attack) the recommendations of an item depending on the intentions of the attacker. Model-based algorithms have performed well under such attacks and are therefore preferred over memory-based CF algorithms despite their lower accuracy. Previous work showed that the recommendation ranking of items using the Robust Weighted Slope One (RWSO) algorithm is fairly stable under profile injection attacks compared to the Weighted Slope One (WSO) and the Improved Slope One (I-SO) approaches. In this paper, it is shown that, under profile injection attacks, the predictive accuracy of RWSO is more stable and hence more reliable than the predictive accuracy of WSO and I-SO.
Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario

Rescue missions require both speed to meet strict time constraints and maximum use of resources. This study presents a Task Swap Allocation (TSA) algorithm that increases vehicle allocation with respect to the state-of-the-art consensus-based bundle algorithm and one of its extensions, while meeting time constraints. The novel idea is to enable an online reconfiguration of task allocation among distributed and networked vehicles. The proposed strategy reallocates tasks among vehicles to create feasible spaces for unallocated tasks, thereby optimizing the total number of allocated tasks. The algorithm is shown to be efficient with respect to previous methods because changes are made to a task list only once a suitable space in a schedule has been identified. Furthermore, the proposed TSA can be employed as an extension for other distributed task allocation algorithms with similar constraints to improve performance by escaping local optima and by reacting to dynamic environments.
Recompressing images to improve image retrieval performance

Virtually all images are stored in compressed form, most in (lossy) JPEG format. Compressing images however has been shown to cause a small but not negligible drop in performance for content-based image retrieval (CBIR) algorithms. In this paper, we show that it is possible to reverse this performance drop. We achieve this by what might at a first glance seem counter-intuitive, namely by compressing the images even more. In detail, what we perform is recompressing images (or rather re-quantising the DCT coefficients) to their lowest common image quality setting. We demonstrate, on a benchmark image retrieval database and using standard CBIR algorithms, that this results in improved image retrieval performance rivalling that of running the algorithms on uncompressed data.
Perioperative cardiac risk prediction

In this study, which is based on data collected from 1351 referred patients, we used myocardial perfusion single-photon emission computed tomography (SPECT) for risk stratification before non-cardiac surgery. We generated binary classifiers using support vector machine (SVM) and conventional linear models for predicting perioperative cardiac events. Input data comprising clinical and surgical risk factors as well as SPECT findings was interpreted to predict the occurrence of all and hard cardiac events. Our SVM models achieved 0.884 and 0.892 in terms of AUC (area under the receiver operating characteristic curve) in predicting all cardiac events and hard events respectively, and were shown to be superior to linear models for risk stratification. We also found an incremental prognostic value of SPECT results over information about clinical and surgical risk.
Efficient 3-D Medical Image Registration Using a Distributed Blackboard Architecture

A major drawback of 3-D medical image registration techniques is the performance bottleneck associated with re-sampling and similarity computation. Such bottlenecks limit registration applications in clinical situations where fast execution times are required and become particularly apparent in the case of registering 3-D data sets. In this paper a novel framework for high performance intensity-based volume registration is presented. Geometric alignment of both reference and sensed volume sets is achieved through a combination of scaling, translation, and rotation. Crucially, resampling and similarity computation is performed intelligently by a set of knowledge sources. The knowledge sources work in parallel and communicate with each other by means of a distributed blackboard architecture. Partitioning of the blackboard is used to balance communication and processing workloads. Large-scale registrations with substantial speedups, when compared with a conventional implementation, have been demonstrated
Rotation invariant compound LBP texture features

Texture is an important characteristic of images and hence used in a variety of computer vision applications. A group of high performing texture algorithms is based on the concept of local binary patterns (LBP) which describe the relationship of pixels to their local neighbourhoods. A rotation invariant form of this descriptor is typically employed since especially for textured surfaces rotation cannot be controlled. Since conventional LBP discards the magnitude information between the centre pixel and neighbouring pixels, Compound LBP (CM-LBP), a variant of LBP, integrates this information by introducing a 16-bit LBP code. The feature length of CM-LBP is then reduced by splitting this 16-bits into two 8-bit codes. However, this approach does not allow for rotation invariant mappings as in conventional LBP, and CM-LBP hence cannot be applied to images under rotation, thus severly limiting the application of the method. In this paper, we address this problem and present rotation invariant and uniform mappings for CM-LBP. We evaluate our new texture descriptor on Outex and Brodatz benchmark datasets and show it to lead to a significantly improved classification performance compared to CM-LBP.
Effective and Efficient Filtering of Retrieved Images Based on JPEG Header Information

Visual information on the web, in particular in form of images, is increasing at a rapid rate. Consequently, efficient and effective techniques to retrieve visual information are sought after, especially as it can be usefully employed to augment textual information. Since users rarely annotate images, this proves to be a challenging task, however much progress has been reported in the area of content-based image retrieval which is based on visual features extracted from images for retrieval purposes. In this paper, we present two strategies for very fast image retrieval which use solely information contained in the header of JPEG compressed files. One is based on the tables that are responsible for the lossy quantisation step in JPEG, while the other is related to the Huffman tables used for entropy coding. In both cases, we employ the tables directly as image features in the context of online image retrieval. We then utilise them to discard irrelevant images, while a compressed-domain image retrieval technique is used for ranking the remaining image set. Experimental results convincingly show that our algorithms lead to a significant reduction of overall retrieval time while maintaining retrieval accuracy. They could thus be integrated into web-based recommender systems to augment and improve search results.
Visualisation of medical infrared image databases

We introduce a method for visualising and navigating through a collection of medical infrared images. Multidimensional scaling is used to provide an overall picture of a given image database by projecting all images on a plane and arranging them so that images that are visually similar are placed close to each other. Navigation through the image set can then be performed by zooming into an area of interest which could correspond to images describing similar symptoms. Experimental results are provided on an image database of 200+ thermal images
HEp-2 Cell Classification Using Multi-scale Texture Information and Multiple Kernel Learning

Indirect immunofluorescence imaging is employed as a standard method to detect antinuclear antibodies in HEp-2 cells which is important for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells are generally categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and centromere cells, which give indications on different autoimmune diseases. Typically, this categorisation is performed manually by an expert and is consequently a time consuming and subjective task. In this paper, we present a method for automatically classifiying HEp-2 cells using multi-scale texture descriptors and multiple kernel learning based classification. We extract local binary pattern (LBP) texture features and summarise these in form of multi-dimensional LBP (MD-LBP) histograms to maintain the relationships between the scales. We then employ a multiple kernel based approach using different support vector machines with polynomial kernels for classification. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate it to outperform all but one of the algorithms that were entered in the competition.
An overview of neural networks in simulation soccer

This paper reviews applications of neural networks (NNs) in the domain of 2-D simulation soccer. We divide these into the employment of NNs for the training of low-level and high-level skills as well as coaching clients involved with high-level strategies. We conclude that the use of NNs has yielded success in these areas, but their future use may be limited to building a foundation of skills which can be advanced through manual hardcoding.
A combined physical and statistical approach to colour constancy

Computational colour constancy tries to recover the colour of the scene illuminant of an image. Colour constancy algorithms can, in general, be divided into two groups: statistics-based approaches that exploit statistical knowledge of common lights and surfaces, and physics-based algorithms which are based on an understanding of how physical processes such as highlights manifest themselves in images. A combined physical and statistical colour constancy algorithm that integrates the advantages of the statistics-based colour by correlation method with those of a physics-based technique based on the dichromatic reflectance model is introduced. In contrast to other approaches not only a single illuminant estimate is provided but a set of likelihoods for a given illumination set. Experimental results on the benchmark Simon Fraser image database show the combined method to clearly outperform purely statistical and purely physical algorithms.
Computed microtomography and x-ray fluorescence analysis for comprehensive analysis of structural changes in bone

This paper presents the results of a comprehensive analysis of structural changes in the caudal vertebrae of Turner's thick-toed geckos by computer microtomography and X-ray fluorescence analysis. We present algorithms used for the reconstruction of tomographic images which allow to work with high noise level projections that represent typical conditions dictated by the nature of the samples. Reptiles, due to their ruggedness, small size, belonging to the amniote and a number of other valuable features, are an attractive model object for long-orbital experiments on unmanned spacecraft. Issues of possible changes in their bone tissue under the influence of spaceflight are the subject of discussions between biologists from different laboratories around the world.
Fast mobile image retrieval

Image collections, in particular those accessed via photo sharing sites, are expanding at a rapid rate. At the same time, internet traffic from mobile devices such as smartphones is growing at a similar speed, and hence efficient and effective techniques for managing and querying these large image repositories from mobile devices are highly sought after. In this paper, we show that this is possible using a very fast method for performing content-based image retrieval of JPEG compressed images that naturally lends itself to mobile retrieval. Our approach performs retrieval directly in the compressed domain of JPEG but, unlike previous techniques, does not require partial decompression of the encoded image data. We employ image adapted Huffman tables, which are stored in the header of JPEG files, as image descriptors and thus not only eliminate the need for decoding but require transfer of only a fraction of the image files. Image similarity is defined as the similarity between DC and AC Huffman table entries, and is shown to lead to good retrieval performance. On a benchmark database, we demonstrate retrieval accuracy similar to common compressed domain and pixel domain retrieval algorithms, yet achieve a speedup of more than 30-fold (compared to JPEG domain techniques) and more than 250-fold (compared to MPEG-7 descriptors) respectively for online image retrieval. A mobile retrieval application is shown to provide an effective way of performing image retrieval from the photo sharing website Flickr while significantly reducing bandwidth and power consumption requirements.
Performance comparison of JPEG compressed domain image retrieval techniques

Although content-based image retrieval (CBIR) has been an active research area for more than two decades, relatively little work takes into account that virtually all images exist in compressed form, mostly in (lossy) JPEG format. In this paper, we benchmark eight state-of-the-art CBIR algorithms that operate directly in the compressed domain of JPEG by performing retrieval based on DCT coefficients. We analyse their performance on a benchmark dataset, compare them against common pixel-domain techniques, investigate whether they are affected by compression ratio, and measure their computational complexity. We conclude that several of the JPEG CBIR techniques allow much faster feature calculation and faster image retrieval, while providing retrieval performance similar to common pixel-domain algorithms.
Automatic Segmentation and Classification of Liver Abnormalities Using Fractal Dimension

Abnormalities in the liver include masses which can be benign or malignant. Due to the presence of these abnormalities, the regularity of the liver structure is altered, which changes its fractal dimension. In this paper, we present a computer aided diagnostic system for classifying liver abnormalities from abdominal CT images using fractal dimension features. We integrate different methods for liver segmentation and abnormality classification and propose an attempt that combines different techniques in order to compensate their individual weaknesses and to exploit their strengths. Classification is based on fractal dimension, with six different features being employed for extracted regions of interest. Experimental results confirm that our approach is robust, fast and able to effectively detect the presence of abnormalities in the liver.
Evolutionary optimisation of classifiers and classifier ensembles for cost-sensitive pattern recognition

Pattern recognition problems occur in many fields and hence effective classification algorithms are the focus of much research. In various circumstances not classification accuracy but misclassification cost minimsation is the primary goal leading to the development of cost-sensitive classification algorithms. In this paper, we show how evolutionary algorithms, in particular genetic algorithms (GAs), can be employed optimise to cost-sensitive classifiers and classifier ensembles. In particular, we discuss how GAs can be employed to derive a compact set of fuzzy if-then rules with an embedded cost term, and how GAs are able to perform simultaneous classifier selection and fusion for ensemble classifiers.
A Cost-based Fuzzy Rule-based System for Pattern Classification Problems

This paper proposes a cost-based fuzzy classification system for pattern classification problems with misclassification costs. The task is to minimize the total misclassification cost incurred by a fuzzy classification system consisting of a number of fuzzy if-then rules where the number of generated fuzzy if-then rules depends on the specification of fuzzy partitions for each axis. In the proposed fuzzy classification system the consequent class of a fuzzy if-then rule is determined so that the misclassification cost is minimal over the covered training patterns by the antecedent part of the rule. On the other hand, conventional fuzzy classification systems are compatibility-based. That is, the consequent class of a fuzzy if-then rule is determined from the compatibility of training patterns covered by the antecedent part of the rule. The grade of certainty of the fuzzy if-then rules in both classification systems is calculated by using the compatibility of training patterns from each class. In a series of computational experiments, we compare the performance of the proposed cost-based fuzzy classification systems with that of the conventional compatibility-based systems. The performance of both classifiers is measured for three real-world pattern classification problems.
Bayesian image segmentation with mean shift

Image segmentation plays a key role in many image content analysis applications, and a lot of effort has aimed at improving the performance of established segmentation algorithms. In this paper, we present a mean shift-based combined Dirichlet process mixture (MDP)/Markov Random Field (MRF) image segmentation algorithm. Our method incorporates a mean shift process to iteratively reduce the difference between the mean of cluster centres and image pixels within the standard MDP/MRF procedure. Experimental results show that the proposed segmentation technique outperforms the classical MDP/MRF algorithm.
A novel image detail preserving impulse noise removal algorithm

In this paper, we introduce a novel edge directed image detail preserving impulse noise removal algorithm. Our approach is based on local image directionality features. Local edge features are analysed on both a downsampled version of the image and the noisy image, and both soft an sharp edges are considered for selective noise removal. Experimental results on a set of standard images show our technique to be effective in removing salt-and-pepper noise even at high noise levels, to yield good image quality and to outperform a number of other noise removal techniques.
Simple and effective pre-processing for automated melanoma discrimination based on cytological findings

In this paper, we propose a simple and effective preprocessing method for melanoma classification by considering cytological properties of melanomas, in particular the alignment of the major axis of the tumor in the same direction. We evaluate our method with a set of 1,760 dermoscopic images (329 of melanomas and 1,431 of nevi) and a simple convolutional neural network (CNN) classifier with five-fold cross validation. The proposed tumor alignment method improves the classification performance by 5.8% in terms of the area under the ROC curve (AUC). In addition, it proves to be 2.1% better in term of AUC when compared with the same configured CNN trained using images that are nine times larger. Our results also show that considering the intrinsic features of the classification target is important even when the classifier has a capability to obtain effective features automatically through its learning process.
Melanoma Classification Using Dermoscopy Imaging and Ensemble Learning

Malignant melanoma, the deadliest form of skin cancer, is one of the most rapidly increasing cancers in the world. Early diagnosis is crucial, since if detected early, it can be cured through a simple excision. In this paper, we present an effective approach to melanoma classification from dermoscopic images of skin lesions. First, we perform automatic border detection to delineate the lesion from the background skin. Shape features are then extracted from this border, while colour and texture features are obtained based on a division of the image into clinically significant regions. The derived features are then used in a pattern classification stage for which we employ a dedicated ensemble learning approach to address the class imbalance in the training data. Our classifier committee trains individual classifiers on balanced subspaces, removes redundant predictors based on a diversity measure and combines the remaining classifiers using a neural network fuser. Experimental results on a large dataset of dermoscopic skin lesion images show our approach to work well, to provide both high sensitivity and specificity, and the use of our classifier ensemble to lead to statistically better recognition performance.