nils-herrmann/clean_scirepeval huggingface dataset:
Fast JPEG compressed domain image retrieval

Content-based image retrieval, which is based on the principle of deriving visual similarity based on extracted image features, can be useful, especially since most images are unannotated. However, while almost all images are stored in compressed form (most in JPEG format), the majority of CBIR algorithms operate in the uncompressed pixel domain. This not only leads to a computational overhead for feature calculation, image compression can also lead to a drop of retrieval accuracy, in particular at extreme compression rates. In this paper, we present efficient and effective CBIR techniques that operate directly in the JPEG compressed domain, hence not requiring full decompression for feature extraction. In particular, we explore how CBIR features can be extracted from DCT coefficients, from differentially coded DC data, and from information contained in the JPEG headers.
Proposal for VCIP 2012 tutorial on “image database visualisation and browsing”

This tutorial discuss the following: Content-based image retrieval - progress and challenges; Retrieval vs. visualisation and browsing; Image database visualisation through dimensionality reduction; Image database visualisation on graphs and networks; Time-based image database visualisation and hybrid visualisation approaches; Horizontal image database browsing; Vertical image database browsing; Immersive image database browsing; Evaluating image database browsing systems;Image database browsing-challenges and future directions.
Scale-invariant vesselness filtering for simultaneous extraction of optimal medial and boundary paths in retinal images

In this paper we present a new multiscale vesselness filtering technique to simultaneously compute optimal medial axes and boundaries on fundus images of the retina. The scale-invariance of the vessel cross-sectional profile in the frequency domain is examined, and phase congruency implementing the scale-invariance is utilised for multiscale vesselness filtering. This allows the vessel ridge and boundary evidence to be preserved under single filtering settings. We have performed width measurement experiments on a dataset of fundus images, using an optimal medial axis skeletonisation scheme as a post-processing step, to compare the proposed technique with a generalised Gaussian profile modelling approach.
Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms

Advances in camera technologies and reduced equipment costs have lead to an increased interest in the application of thermography in the medical fields. Thermography is of particular interest for detection of breast cancer as it has been shown that it is capable of detecting the cancer earlier and is also allows diagnosis of fatty breast tissue. In this paper we perform breast cancer detection based on thermography, using a series of statistical features extracted from the thermograms coupled with a fuzzy rule-based classification system for diagnosis. The features stem from a comparison of left and right breast areas and quantify the bilateral differences encountered. Following this asymmetry analysis the features are fed to a fuzzy classification system. This classifier is used to extract fuzzy if-then rules based on a training set of known cases. Experimental results on a set of nearly 150 cases show the proposed system to work well accurately classifying about 80% of cases, a performance that is comparable to other imaging modalities such as mammography.
Weighted fuzzy classification with integrated learning method for medical diagnosis

Medical diagnosis can be viewed as a pattern classification problem: based a set of input features the goal is to classify a patient as having a particular disorder or as not having it. Performance of medical diagnosis is typically assessed in terms of sensitivity and specificity. In this paper we introduce a pattern classification system for medical diagnosis that is based on fuzzy logic and utilises weighted training patterns. Adjusting the weights allows to focus either on sensitivity or specificity while not neglecting the other one and hence lends a degree of flexibility to the diagnostic process. A learning method is utilised that provides improved classification performance. Excellent classification results based on the University of Wisconsin breast cancer database are presented
Features of detection of a single-photon pulse at synchronisation in quantum key distribution systems

The object of this study is a two-pass fiber-optic quantum key distribution system (QKDS) with phase coding states of photons in synchronisation mode. The QKDS is based on a scheme with automatic compensation of polarisation distortions. We use single-photon avalanche photodiodes (SPAD) as optical radiation detection devices. We also evaluate the impact of parameters used in QKDS optical radiation detectors on probabilistic and time characteristics of the detected time interval with a weak optical pulse in sync. In this paper, we describe the methodology of the design process to detect a time interval with an optical pulse during synchronisation taking into account the features used in QKDS SPAD. The aim of our work is to evaluate the impact of QKDS changing hardware synchronisation settings on probabilistic and time characteristics of the detection time interval. We describe an algorithm that we have developed to synchronise a QKDS that considers the time to restore the operating state of a photon after registration during synchronisation. For evaluation, we report computer modelling results of the design process to detect a time interval according to the changing parameters applied in SPAD quantum key distribution systems.
Fast JPEG Image Retrieval Based on AC Huffman Tables

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 since users rarely annotate images. In this paper, we present a very fast method for content-based image retrieval of JPEG compressed images. Our method works directly in the compressed domain of JPEG and is based solely on information available in the image header. We make use of the fact that the Huffman tables that JPEG uses for entropy coding can be optimised to maximise compression. Since this process adapts the Huffman tables to the image content, we utilise the tables directly, in particular the prefix code lengths of AC luminance and chrominance table entries, as image features, and employ the L1 distance between the code length vectors as similarity measure. We evaluate our method on benchmark databases of varying sizes up to in excess of 1 million images, and show that our approach provides good retrieval performance, while providing a more than 30-fold speedup compared to JPEG compressed domain algorithms and more than 150-fold compared to common pixel domain techniques for online image retrieval.
Texture classification using compact multi-dimensional local binary pattern descriptors

Texture analysis and classification plays an important role in many computer vision systems and applications. Local binary patterns (LBP) form a simple yet powerful texture descriptor characterising local neighbourhood properties, which, due to its effectiveness and robustness, is widely used. LBP descriptors can also be recorded at different radii leading to multiscale features. While in conventional LBP, this information is recorded, in form of a histogram, separately for each of the scales, it was shown that a multi-dimensional feature representation removes some ambiguity and leads to better texture classification. However, these multi-dimensional LBP (MD-LBP) histograms also make rather large feature descriptors which limits their practical use. In this paper, we show that we can effectively compactify the information contained in MD-LBP histograms, and show, through extensive experiments on seven Outex datasets, that with the same feature length as the original LBP method we are able to obtain clearly improved texture classification which closely matches that achieved by the full MD-LBP descriptor.
A Self-Adaptive Hybrid Genetic Algorithm for Color Clustering

Color palettes are inherent to color quantized images and represent the range of possible colors in such images. When converting full true color images to palletized counterparts, the color palette should be chosen so as to minimize the resulting distortion compared to the original. In this paper, we show that in contrast to previous approaches on color quantization, which rely on either heuristics or clustering techniques, a generic optimization algorithm such as a self-adaptive hybrid genetic algorithm can be employed to generate a palette of high quality. Experiments on a set of standard test images using a novel self-adaptive hybrid genetic algorithm show that this approach is capable of outperforming several conventional color quantization algorithms and provide superior image quality.
Mammary Gland Tumor Detection in Cats Using Ant Colony Optimisation

Mammary gland tumors are among the most common tumors in cats. Over 85 percent of mammary tumors in cats are malignant and they tend to grow and metastasize quickly to different organs like lungs and lymph nodes. Similar to breast tumors in humans, they start as a small lump in a mammary gland and then grow and increase in size unless detected and treated. In this paper, we present an approach to detect broadenoma mammary gland tumors in cats using ant colony optimisation. Image features can then be extracted from the segmented image regions. To evaluate the performance of our presented approach, 25 microscopical images were taken from tissue slides of broadenomas from three cat cases. The experimental results obtained confirm that the effectiveness and performance of the proposed system is high.
Historic handwritten manuscript binarisation using whale optimisation

Preserving the content of historic handwritten manuscripts is important for a variety of reasons. On the other hand, digital libraries are rapidly expanding and thus facilitate to store this information directly in digital form. For digitising text documents, a crucial step is to binarise the captured images to separate the text from the background. In this paper, we propose an effective approach for binarisation of handwritten Arabic manuscripts which employs a whale optimisation algorithm, incorporating a fuzzy c-means objective function, to obtain optimal thresholds. Experimental results confirm the effectiveness of the proposed approach compared to earlier methods.
Hybrid cost-sensitive fuzzy classification for breast cancer diagnosis

Breast cancer is the most commonly diagnosed form of cancer in women accounting for about 30% of all cases. From a computational point of view, breast cancer diagnosis can be viewed as a pattern classification problem. In this paper, we present a cost-sensitive approach to classifying breast cancer data. In particular, we employ a fuzzy rule base that allows incorporation of a misclassification cost term in order to provide the ability to focus on certain classes and hence to boost the identification of malignant cases. Moreover, we show how genetic algorithms can be employed to optimise a compact yet effective rule base, investigating both Michigan and Pittsburgh style approaches of hybrid GA-fuzzy classifiers in the context of breast cancer diagnosis.
A statistical method for estimating the accuracy of scene reconstruction using a collinear digital stereo vision system

We present a model and technique for the analysis of measurement capabilities of on-board collinear digital stereo vision systems. For this, we use a technique of determining statistical distributions of measurement errors and report the obtained results. The proposed model can be employed for pre-calculating the characteristics of on-board collinear digital stereo vision systems, while the results can also be used to estimate the accuracy of reconstructed maps. This is especially interesting for the oil industry, where continuous monitoring of the conditions of long pipelines using autonomous robots is necessary. With the help of appropriate stereo vision systems, it is possible to assess the conditions of pipelines, detect critical deformations, fix gust places, etc. to ensure safety in the oil infrastructure.
Skin lesion segmentation using an improved snake model

Accurate identification of lesion borders is an important task in the analysis of dermoscopy images since the extraction of skin lesion borders provides important cues for accurate diagnosis. Snakes have been used for segmenting a variety of medical imagery including dermoscopy, however, due to the compromise of internal and external energy forces they can lead to under- or over-segmentation problems. In this paper, we introduce a mean shift based gradient vector flow (GVF) snake algorithm that drives the internal/external energies towards the correct direction. The proposed segmentation method incorporates a mean shift operation within the standard GVF cost function. Experimental results on a large set of diverse dermoscopy images demonstrate that the presented method accurately determines skin lesion borders in dermoscopy images.
An overview of rough-hybrid approaches in image processing

Rough set theory offers a novel approach to manage uncertainty that has been used for the discovery of data dependencies, importance of features, patterns in sample data, feature space dimensionality reduction, and the classification of objects. Consequently, rough sets have been successfully employed for various image processing tasks including image segmentation, enhancement and classification. Nevertheless, while rough sets on their own provide a powerful technique, it is often the combination with other computational intelligence techniques that results in a truly effective approach. In this paper we show how rough sets have been combined with various other methodologies such as neural networks, wavelets, mathematical morphology, fuzzy sets, genetic algorithms, Bayesian approaches, swarm optimization, and support vector machines in the image processing domain.
Real-time diagnostics of oil production equipment using data mining

In this paper, we consider the problem of increasing the efficiency of heavy oil production by a qualitative enhanced oil recovery (EOR) application. A novel approach, based on the analysis of data related to successful events is proposed. In particular, a synthesised model, targeted to automated search of wells for EOR application, is developed. For data processing, we present a novel approach based on a hybrid implementation of neural network analysis techniques and evolutionary algorithms. The presented approach enables the selection of EOR in fuzzy, difficult to formalise, oilfield conditions and consequently reduces the dependency on human factors.
Ensemble fusion methods for medical data classification

Medical data classification is acknowledged as an area of increasing importance, yet also poses many difficulties. One of these is that medical datasets are often imbalanced; that is that there are (potentially many) more samples of some classes compared to others. In this paper, a dedicated algorithm - Undersampling Balanced Ensemble (USBE) - is used to deal with this problem. We then conduct an experimental study to investigate the quality of different fusion methods for combining classifiers in an ensemble. Several fusion techniques based on discrete and continuous responses from (neural network) base classifiers are evaluated and it is shown that a careful choice of fusion method can boost the recognition rate of the minority class. In particular, a neural network trained fuser is shown to provide the best classification performance on two separate breast cancer datasets.
A comparative analysis of local binary pattern texture classification

Texture recognition is an important aspect of many computer vision applications. Local binary pattern (LBP) based texture algorithms have gained significant popularity in recent years and have been shown to be useful for a variety of tasks. While over the years a variety of LBP algorithms have been introduced in the literature, what is missing is a comprehensive evaluation of their performance. In this paper, we fill this gap and benchmark 37 texture descriptors based on 15 LBP variants for texture classification against common standard datasets of textures including those captured at different rotation angles and under different illumination conditions. Overall, LBP variance (LBPV) is found to give the best texture classification performance.
Head pose estimation in the wild using approximate view manifolds

In this paper, we present a head pose estimation method for unconstrained images using feature-based manifold embedding. The main challenge of manifold embedding methods is to learn a similarity kernel that is reflective of variations only due to head pose and ignore other sources of variation. To address this challenge, we have used the feature correspondences of identity-invariant Geometric Blur features to learn a similarity kernel. To speed up the computation of the similarity kernel, we have used spatial pyramidal matching to approximate feature correspondences and random subsampling of training samples to approximate graph neighborhood. In addition to these approximations, we have used the Nyström approximation to embed out-of-sample test images in an efficient manner. Using these approximations, an approximate view manifold was learned for 14000 images in the Annotated Facial Landmarks in the Wild (AFLW) dataset. With the learned manifold, head pose estimation was performed on four in-the-wild face datasets - AFLW (remaining 7000 images), AFW, McGill and YouTube Faces. The Approximate View Manifold training achieves a 7X speedup compared to the non-approximated Learning-manifold-in-the-wild approach . Further, pose estimation using the proposed approach shows significant improvement in accuracy and reduced Mean Angular Error(MAE) compared to other methods [36, 1, 29] on the challenging AFLW (7041 images), McGill (6833 images) and YouTube Faces (22534 images) datasets.
On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery

Human recognition based on the iris biometric is severely impacted when encountering non-ideal images of the eye characterized by occluded irises, motion and spatial blur, poor contrast, and illumination artifacts. This paper discusses the use of the periocular region surrounding the iris, along with the iris texture patterns, in order to improve the overall recognition performance in such images. Periocular texture is extracted from a small, fixed region of the skin surrounding the eye. Experiments on the images extracted from the Near Infra-Red (NIR) face videos of the Multi Biometric Grand Challenge (MBGC) dataset demonstrate that valuable information is contained in the periocular region and it can be fused with the iris texture to improve the overall identification accuracy in non-ideal situations.
SSGA & EDA based feature selection and weighting for face recognition

In this paper, we compare genetic and evolutionary feature selection (GEFeS) and weighting (GEFeW) using a number of biometric datasets. GEFeS and GEFeW have been implemented as instances of Steady-State Genetic and Estimation of Distribution Algorithms. Our results show that GEFeS and GEFeW dramatically improve recognition accuracy as well as reduce the number of features needed for facial recognition. Our results also show that the Estimation of Distribution Algorithm implementation of GEFeW has the best overall performance.
A gender-specific behavioral analysis of mobile device usage data

Mobile devices provide a continuous data stream of contextual behavioral information which can be leveraged for a variety of user services, such as in personalizing ads and customizing home screens. In this paper, we aim to better understand gender-related behavioral patterns found in application, Bluetooth, and Wi-Fi usage. Using a dataset which consists of approximately 19 months of data collected from 189 subjects, gender classification is performed using 1,000 features related to the frequency of events yielding up to 91.8% accuracy. Then, we present a behavioral analysis of application traffic using techniques commonly used for web browsing activity as an alternative data exploration approach. Finally, we conclude with a discussion on impersonation attacks, where we aim to determine if one gender is less vulnerable to unauthorized access on their mobile device.
Mobile device application, Bluetooth, and Wi-Fi usage data as behavioral biometric traits

Patterns in the use of mobile devices have the potential to be used as a behavioral biometric for identification of the device user. We explore the distinctiveness and permanence of application, Bluetooth, and Wi-Fi mobile device usage data. Our database consists of data from two hundred mobile phone users collected over a 19-month span. To our knowledge, this is one of the largest databases of its kind. Results of over 500 experiments indicate that user identification rates averaging 80%, 77%, 93%, and 85% are achievable when using application, Bluetooth, Wi-Fi, and the combination of these three types of behavioral features, respectively.
Genetic & Evolutionary Type II feature extraction for periocular-based biometric recognition

One of the most important modules of any bio-metric system is the feature extraction module. Given a sample it is important for the feature extraction method to extract a rich set of features that can be used for identity recognition. This form of feature extraction has been referred to as Type I feature extraction and for some biometric systems it is used exclusively. However, a second form of feature extraction does exist and is concerned with optimizing/minimizing the original feature set given by a Type I feature extraction method. This second form of feature extraction has been referred to as Type II feature extraction (also known as feature selection). In this paper, we compare two GEC-based Type II feature extraction methods as applied to periocular-based recognition, an exciting new area of research within the Biometric research community that to date has used Type I feature extraction exclusively. Our results show that GEC-based Type II feature extraction is effective in optimizing recognition accuracy as well as minimizing the overall feature set size.
Soft biometric classification using periocular region features

With periocular biometrics gaining attention recently, the goal of this paper is to investigate the effectiveness of local appearance features extracted from the periocular region images for soft biométrie classification. We extract gender and ethnicity information from the periocular region images using grayscale pixel intensities and periocular texture computed by Local Binary Patterns as our features and a SVM classifier. Results are presented on the visible spectrum periocular images obtained from the FRGC face dataset. For 4232 periocular images of 404 subjects, we obtain a baseline gender and ethnicity classification accuracy of 93% and 91%, respectively, using 5-fold cross validation. Furthermore, we show that fusion of the soft biométrie information obtained from our classification approach with the texture based periocular recognition approach results in an overall performance improvement.