nils-herrmann/clean_scirepeval huggingface dataset:
Strategies for addressing class imbalance in ensemble classification of thermography breast cancer features

Thermography provides an interesting alternative to mammography for diagnosing breast cancer as it is a noncontact, non-invasive and passive technique that is able to detect small tumors and thus can lead to earlier diagnosis. Computer-aided diagnostic approaches based on thermography are typically split into a feature extraction stage that derives useful information from the thermogram images, and a classification stage that distinguishes between malignant and benign cases. The latter is challenging since, as is the case for many medical decision making problems, there are (many) more benign cases available for classifier training compared to malignant cases, leading to an imbalanced classification problem. In this paper, we first perform image analysis to identify features describing bilateral differences in regions of interest in the thermogram. These features then form the input for a pattern classification stage for which we present several strategies to address the existing class imbalance in the context of ensemble classifiers. In particular, we discuss an ensemble constructed of cost-sensitive decision tree classifiers, an ensemble whose base classifiers are trained on balanced subspaces, and an ensemble that is based on the combination of one-class classifiers. All three strategies are evaluated on a challenging dataset of about 150 thermograms and it is shown that they provide very good classification performance and furthermore perform favourably compared to other state-of-the-art classifier ensembles for imbalanced data.
Adopting the DICOM standard for medical infrared images

In recent years there has been a resurgence of interest in the application of infrared thermal imaging in medicine. Yet fairly little effort has been spent towards standardisation of the field and a common communication and exchange format for thermal images. Most other medical areas where digital imaging is employed have subscribed to DICOM (Digital Imaging and Communication in Medicine) as a common standard for storing and retrieving medical imagery. In this paper we investigate how the DICOM standard in its current form can be adopted to store and communicate medical infrared images
Efficient Filtering of JPEG Images

With image databases growing rapidly, efficient methods for content-based image retrieval (CBIR) are highly sought after. In this paper, we present a very fast method for filtering JPEG compressed images to discard irrelevant pictures. We show that compressing images using individually optimised quantisation tables not only maintains high image quality and therefore allows for improved compression rates, but that the quantisation tables themselves provide a useful image descriptor for CBIR. Visual similarity between images can thus be expressed as similarity between their quantisation tables. As these are stored in the JPEG header, feature extraction and similarity computation can be performed extremely fast, and we consequently employ our method as an initial filtering step for a subsequent CBIR algorithm. We show, on a benchmark dataset of more than 30,000 images, that we can filter 80% or more of the images without a drop in retrieval performance while reducing the online retrieval time by a factor of at about 5.
Efficient and effective online image retrieval

With visual information becoming increasingly important, efficient and effective methods for querying and retrieving this kind of information are highly sought after. In this paper, we focus on image information and querying from image collections in an online retrieval fashion. In online retrieval, image features for performing retrieval are not pre-calculated but need to be extracted during the retrieval stage. Consequently, and in particular for large image datasets, the time required for feature extraction becomes crucial so as to not exceed interactive retrieval speeds. Our aim in this work is to match the retrieval accuracy of a high performing yet rather slow image retrieval method, but perform the retrieval in only a fraction of the time. We achieve this by a combination of two carefully crafted filtering stages both of which are based on the way data is stored in JPEG compressed images. The first of these performs extremely fast image retrieval using solely information contained in the JPEG headers. The second stage employs a compressed domain retrieval method that utilises features calculated from JPEG coefficient data. The first filter discards a large part of irrelevant images in a very fast fashion. The remaining images are filtered by the second technique in order to arrive at a relatively small subset of the complete database in a timely fashion. Finally, on this subset the high performing algorithm of choice, the MPEG-7 colour structure descriptor in this paper, is applied to produce a final ranking of the images to be returned to the user. Our experimental results demonstrate that on a large dataset of over 25,000 images our approach achieves retrieval scores nearly identical to those of the high performing technique while reducing the overall retrieval time by a factor of 15.
Improved LBP texture classification using ensemble learning

Texture analysis and classification play an important role in many multimedia and computer vision applications. Local binary patterns (LBP) form a simple yet powerful texture descriptor characterising local neighbourhood properties, and consequently LBP variants are widely employed. In this paper, we demonstrate that through appropriate construction of a multiple classifier system, improved texture classification based on LBP features is possible. In particular, we employ a classifier ensemble where each classifier (a support vector machine) is trained in conjunction with a different feature selection method. The ensemble is then pruned based on a diversity measure, and the remaining models are combined using a neural fuser. Experimental results, obtained on Outex benchmark datasets and employing four LBP variants, confirm that our proposed approach leads to statistically significantly improved texture classification.
A browsing approach to online photo search results

Image sharing Web sites are constantly growing in size, containing up to several billion photographs. With such large databases, effective tools are needed in order to browse these sites. Search using keywords, tags and albums provide a basic level of filtering but is not sufficient to handle the thousands of results that are typically returned. Consequently, there is a need to go beyond image metadata, taking into account the image content itself to perform integrated browsing based on visual characteristics as well as keywords. While various approaches to visualisation and browsing of image databases have been introduced, these are too slow to be applied to online retrieval results, since the complete image files would need to be downloaded for feature extraction and exploitation. In this paper, we present a method that addresses this problem whilst being entirely client-side. We construct an interactive image database navigation application that uses the Huffman table in JPEG files as features, and projects image thumbnails onto a 2-dimensional visualisation space based on principal component analysis derived from the Huffman entries. Images are dynamically placed into a grid structure and organised in a tree-like hierarchy for visual browsing. Since we utilise information only from the JPEG header, we significantly reduce the required bandwidth and thus allow for interactive browsing of photo search results.
Comparison of simulated annealing and SASS for parameter estimation of biochemical networks

Estimating the parameters of biochemical networks from time-courses is becoming increasingly important. There have been some attempts in the past to carry out this task in an automatic way. In this research, SASS, a novel heuristic optimisation algorithm that has only one control parameter, has been used to solve this problem. While the obtained estimations are similar to those using other recent techniques, the method presented here offers a better resistance to local minima and a decrease of a 20% in average in computational cost, without the need of finding a suitable set of control parameters.
Exploiting JPEG Compression for Image Retrieval

Content-based image retrieval (CBIR) has been an active research area for many years, yet much of the research ignores the fact that most images are stored in compressed form which affects retrieval both in terms of processing speed and retrieval accruacy. In this paper, we address various aspects of JPEG compressed images in the context of image retrieval. We first analyse the effect of JPEG quantisation on image retrieval and present a robust method to address the resulting performance drop. We then compare various retrieval methods that work in the JPEG compressed domain and finally propose two new methods that are based solely on information available in the JPEG header. One of these is using optimised Huffman tables for retrieval, while the other is based on tuned quantisation tables. Both techniques are shown to give retrieval performance comparable to existing methods while being magnitudes faster.
Cost-Sensitive Fuzzy Classification for Medical Diagnosis

Medical diagnosis essentially represents a pattern classification problem: based on a certain input an expert arrives at a diagnosis which often takes on a binary form, i.e. the patient suffering from a certain disease or not. A lot of research has focussed on computer assisted diagnosis where objective measurements are passed to a classifier algorithm which then proposes diagnostic output based on a previous learning process. However, these classifiers put equal emphasis on a learning patterns irrespective of the class they belong to. In this paper we apply a fuzzy rule-based classification system to medical diagnosis. Importantly, we extend the classifier to incorporate a concept of cost which can be used to emphasize those cases that signify illness as it is usually more costly to incorrectly diagnose such a patient as being healthy. Experimental results on various medical datasets confirm the usefulness and efficacy of our approach
Application of cost-sensitive fuzzy classifiers to image understanding problems

Image understanding applications often involve a pattern classification stage. In this paper we show how a fuzzy rule-based classifier, extended to incorporate a cost function, can be successfully used in various imaging applications. The antecedent part of fuzzy if-then rules are specified by partitioning each attributes into fuzzy sets while the consequent class and the degree of certainty are determined from compatibility training patterns. Extension to include a cost term is shown to be straightforward and experimental results on several image processing tasks demonstrate the efficacy of our method.
Content-based image retrieval for medical infrared images

Past efforts on the automated processing on medical infrared images has typically focused on specialized applications like the detection of breast cancer. We propose the application of content-based image retrieval (CBIR) to medical thermal images. CBIR allows the retrieval of similar images based on features directly extracted from the image data. Hence, image retrieval for a thermal image that shows symptoms of a certain disease will provide visually similar cases which will usually also represent similarities in medical terms. The image features we propose for this purpose are a set of moment invariants of the grayscale thermal images.
Lossless Colour Image Coding Using the Mojette Transform and Different Projections

The Mojette transform is a discrete, exact and redundant Radon transform. The application of the Mojette transform for lossless image compression is based on image projection similarity using different directions, with intra-projection coding, inter-projection coding and differential coding schemes being applied. For the latter case, we propose mean coding of projections to improve the Mojette transform with different projections, and we calculate the difference between the projections and the mean. Experimental results obtained are shown to be better compared to inter-projection coding, and comparable with CALIC.
Hybrid Fuzzy Rule-Based Classification

Many real world applications contain a decision making process which can be regarded as a pattern classification stage. Various pattern classification techniques have been introduced in the literature ranging from heuristic methods to intelligent soft computing techniques. In this paper, we focus on the latter and in particular on fuzzy rule-based classification algorithms.We show how an effective classifier employing fuzzy if-then rules can be generated from training data, and highlight how the introduction of class weights can be used for costsensitive classification. We also show how a training algorithm can be applied to tune the classification performance and how genetic algorithms can be used to extract a compact fuzzy rule base. We also give pointers to various applications where these methods have been employed successfully.
Intuitive mobile image browsing on a hexagonal lattice

Following miniaturisation of cameras and their integration into mobile devices such as smartphones combined with the intensive use of the latter, it is likely that in the near future the majority of digital images will be captured using such devices rather than using dedicated cameras. Since many users decide to keep their photos on their mobile devices, effective methods for managing these image collections are required. Common image browsers prove to be only of limited use, especially for large image sets .
Fast Compressed Domain JPEG Image Retrieval

While much progress has been achieved in the field of content-based image retrieval (CBIR), almost all CBIR techniques operate on pixel data although virtually all images are stored in compressed form. In this invited paper, we present efficient and effective CBIR techniques that operate directly in the compressed domain and thus do not require full decompression for feature extraction. In particular, we explore compressed domain techniques for JPEG images and show how CBIR features can be extracted from DCT coefficients, from differentially coded DC data, and from optimised Huffman and quantisation tables that are stored in the JPEG headers.
Robust border detection in dermoscopy images using threshold fusion

Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. In many cases, the lesion can be roughly separated from the background skin using a thresholding method applied to the blue channel. However, no single thresholding method appears to be robust enough to successfully handle a wide variety of dermoscopic images. In this paper, we present an automated method for detecting lesion borders in dermoscopy images using a fusion of several thresholding methods. Experiments on a difficult set of 90 images demonstrate that the proposed method achieves both fast and accurate results when compared to six state-of-the-art methods.
Automated color normalization for dermoscopy images

Accurate color information in dermoscopy images is very important for melanoma diagnosis since inappropriate white balance or brightness in the images adversely affects the diagnostic performance. In this paper, we present an automated color normalization method for dermoscopy images of skin lesions. We develop color normalization filters based on a total of 319 images which normalize color of images using the HSV color system. We determined that the color characteristics of the peripheral part of the tumors significantly influence the color normalization and confirmed that the developed normalization filter achieved satisfactory normalization performance as evaluated by a cross-validation test.
Truly Interactive Approaches to Browsing Image Databases

Image collections are ever growing and hence efficient and effective tools to manage these repositories are highly sought after. In this paper, we present effective image browsing systems that are operated on a large multi-touch environment for truly interactive exploration. Not only do image browsers pose a useful alternative to retrieval-based systems, they also provide a visualisation of the whole image collection and allow users to interactively explore particular parts of the collection. Our systems are based on the idea that visually similar images are located close to each other in the visualisation, that image thumbnails are arranged on a regular lattice (either a regular grid projected onto a sphere or a hexagonal lattice), and that large image datasets can be accessed through a hierarchical tree structure. A pilot study has shown that the presented systems do indeed work well and are preferred compared to conventional image browsers.
Automatic Classification of HEp-2 Cells Using Multi-dimensional Local Binary Patterns

Indirect immunofluorescence imaging is a fundamental technique used for detecting antinuclear antibodies in HEp-2 cells. This is particularly useful for the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells can be categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and Centro mere cells, which give indications of different autoimmune diseases. This categorisation is typically performed through manual evaluation which is time consuming and subjective. In this paper, we propose a method for automatic classification of HEp-2 cells using local binary pattern (LBP) based texture descriptors. LBP is a simple yet powerful texture algorithm which encodes the relationship of pixels to their local neighbourhood. In our approach, we utilise multi-dimensional LBP (MD-LBP) histograms, which record multi-scale texture information while maintaining the relationships between the scales, and perform classification using support vector machines. We demonstrate our algorithm to work well on a dataset of 721 cell images, giving a correct classification rate exceeding 95%, which is particularly impressive as it is based solely on a single type of image feature.
An Improved Binarisation Algorithm for Nailfold Capillary Skeleton Extraction

Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nail fold, and is routinely used for the detection of scleroderma spectral disorders, Raynaud's phenomenon and other connective tissue diseases. While NC image evaluation is typically performed through manual inspection by an expert, computer-aided approaches of capillary inspection can reduce the time required for diagnosis. The aim of NC image analysis is usually to extract the skeletons of the capillaries present in the image which form the basis of further analysis for diagnosis. In this paper, we propose an improved binarisation technique for NC image analysis that addresses the challenge of non-uniform background by employing a Difference-of-Gaussian approach before thresholding, coupled with a post-processing stage to remove smaller image artefacts. Based on two previously published NC skeletonisation algorithms, we demonstrate that our technique leads to significantly better skeleton extraction on different types of NC images.
Modelling of oil spill spread

This paper presents a model of oil spill spread taking into account the evaporation of light, neutral and non-evaporating pseudofractions of oil spots, dissolution of oil spots and biodegradation. An approximation of the diffusion-convection problem is performed based on schemes of high order of accuracy. An experimental implementation, designed to model possible ecosystem scenarios of shallow waters for oil spills in the Azov-Black Sea basin, is presented. Based on a multi-processor architecture, decomposition methods of grid domains have been employed for computationally expensive diffusion-convection tasks in the context of a parallel implementation. The maximum speed-up achieved is by a factor of about 230 based on a configuration of 512 computational nodes.
A novel method for distribution of goals among UAVs for oil field monitoring

Our aim in this paper is to improve the efficiency of unmanned aerial vehicles (UAVs) that are employed in a group to monitor large territories and search for various objects. The problem is that of distributing a certain set of scan areas between the members of a UAV group so as to minimise the scan time. Our proposed approach allows to solve the problem of distribution of targets (scan areas) between the UAVs through control devices of separate UAVs, combined with communication channels in a computer network, and is applicable for a variety of different classes of tasks in groups of UAVs. The proposed method allows to minimise the possible intersection of UAV trajectories and to reduce the probability of collisions of UAVs. Experimental simulation results confirm the efficiency of the proposed approach and show that it is possible to obtain close to optimal variants of the distribution of scan areas between UAVs group from the viewpoint of minimising the total time and safe movement of UAVs to targets.
Retinal vessel extraction using a piecewise Gaussian scaled model

Automated analysis of retinal images usually requires estimating the positions and appearance of blood vessels, which contain important features for abnormality detection. Although there is a wide literature on detecting vessel positions from retinal images by modelling cross-sectional profiles, little attention has been given to extracting vessel appearance in intensity. In this paper, we introduce a piecewise Gaussian scaled model to characterise the intensity distributions of vessel cross-sections. Based on a newly developed vessel detection scheme, we describe the use of the proposed model for extracting vessel appearance. The preliminary experimental results obtained from angiographic pairs and images of an SLO sequence are reported.
Cost-sensitive texture classification

Texture recognition plays an important role in many computer vision tasks including segmentation, scene understanding and interpretation, medical imaging and object recognition. In some situations, the correct identification of particular textures is more important compared to others, for example recognition of enemy uniforms for automatic defense systems, or isolation of textures related to tumors in medical images. Such cost-sensitive texture classification is the focus of this paper, which we address by reformulating the classification problem as a cost minimisation problem. We do this by constructing a cost-sensitive classifier ensemble that is tuned using a genetic algorithm. Based on experimental results obtained on several Outex datasets with cost definitions, we show our approach to work well in comparison with canonical classification methods and the ensemble approach to lead to better performance compared to single predictors.