nils-herrmann/clean_scirepeval huggingface dataset:
STTK-based video object recognition

In this paper, we extend our video object recognition system to multiclass object recognition context, dealing with unbalanced data sets and comparing our resuls to state-of-the-art methods. Our approach is based on a Spatio-Temporal data representation, a dedicated kernel design and statistical learning techniques for object recognition. From video tracks made of segmented object regions in the successive frames, we extract sets of spatio-temporally coherent SIFT-based features, called Spatio-Temporal Tubes. To compare these complex tube objects, we integrate a Spatio-Temporal Tube Kernel (STTK) function into a multi-class classification framework with balancing process for unequal classes. Our approach is successfully evaluated on episodes from “Buffy, the Vampire Slayer” TV series which have been used in other works targeting same objectives. Our method proved to be more robust than dictionary based, facial feature based and key-frame based approaches. Our method is also tested on a small car database and preliminary results for car identification task illustrate its generalization potential.
Improving SVM Training Sample Selection Using Multi-Objective Evolutionary Algorithm and LSH

In this paper, we propose a new framework hybridizing a Support Vector Machine (SVM), a Multi-Objective Genetic Algorithm (MOGA) and a Locality Sensitive Hashing (LSH). The goal is to tackle fine-grained classification challenges which means classifying many classes with high similarities between classes and poor similarities inside one class. SVM is used for its ability of learning multi-class problem from very few training data. MOGA is used for optimizing training samples used by SVM so as to improve its learning rate. As data define a discrete set of instances and not a continuous solution space, LSH is used for mapping "optimal solutions" obtained by MOGA onto the closest real instances contained in the dataset. We evaluate our method for content-based image classification on the standard image database Caltech256 (i.e. 30000 images distributed in 256 classes). Experiments shows that our method outperforms reference approaches.
Boosting kernel combination for multi-class image categorization

In this paper, we propose a novel algorithm to design multi-class kernel functions based on an iterative combination of weak kernels in a scheme inspired from boosting framework. The method proposed in this article aims at building a new feature where the centroid for each class are optimally located. We evaluate our method for image categorization by considering a state-of-the-art image database and by comparing our results with reference methods. We show that on the Oxford Flower databases our approach achieves better results than previous state-of-the-art methods.
Alpha-divergence maximization for statistical region-based active contour segmentation with non-parametric PDF estimations

In this article, a complete original framework for unsupervised statistical region-based active contour segmentation is proposed. More precisely, the method is based on the maximization of alpha-divergences between non-paramterically estimated probability density functions (PDFs) of the inner and outer regions defined by the evolving curve. We define the variational context associated to distance maximization in the particular case of alpha-divergences and provide the complete derivation of the partial differential equation leading the segmentation. Results on synthetic data, corrupted with a high level of Gaussian and Poisson noises, but also on clinical X-ray images show that the proposed unsupervised method improves standard approaches of that kind.
Recipe recognition with large multimodal food dataset

This paper deals with automatic systems for image recipe recognition. For this purpose, we compare and evaluate leading vision-based and text-based technologies on a new very large multimodal dataset (UPMC Food-101) containing about 100,000 recipes for a total of 101 food categories. Each item in this dataset is represented by one image plus textual information. We present deep experiments of recipe recognition on our dataset using visual, textual information and fusion. Additionally, we present experiments with text-based embedding technology to represent any food word in a semantical continuous space. We also compare our dataset features with a twin dataset provided by ETHZ university: we revisit their data collection protocols and carry out transfer learning schemes to highlight similarities and differences between both datasets. Finally, we propose a real application for daily users to identify recipes. This application is a web search engine that allows any mobile device to send a query image and retrieve the most relevant recipes in our dataset.
One gaze is worth ten thousand (key-)words

With the maturity of machine learning methods to provide satisfying Content-Based Image Retrieval systems (CBIR), research focus has recently turned back towards visual saliency analysis. The goal in these works is to extract even more efficient visual features than the existing ones. However, analyzing visual saliency is critically dependent on the task to be accomplished from the extracted visual features. A significant number of CBIR systems consider image retrieval as a binary classification problem: what is relevant for the user against what is irrelevant. In this paper, we focus on extracting relevant gaze features within the paradigm of visual preference in order to support the annotation by gaze for a CBIR system. We thus define a gaze acquisition protocol, design a benchmark from a subset of Pascal VOC database and present an in depth analysis of eye-tracking data for visual preference paradigm. Our paper provides new informations on relevant gaze features for image binary classification.
Spatio-Temporal Tube Kernel for actor retrieval

This paper presents an actor video retrieval system based on face video-tubes extraction and representation with sets of temporally coherent features. Visual features, SIFT points, are tracked along a video shot, resulting in sets of feature point chains (spatio-temporal tubes). These tubes are then classified and retrieved using a kernel-based SVM learning framework for actor retrieval in a movie. In this paper, we present optimized feature tubes, we extend our feature representation with spatial location of SIFT points and we describe the new Spatio-Temporal Tube Kernel (STTK) of our content-based retrieval system. Our approach has been tested on a real movie and proved to be faster and more robust for actor retrieval task.
A genetic algorithm-based approach to knowledge-assisted video analysis

Efficient video content management and exploitation requires extraction of the underlying semantics, a non-trivial task associating low-level features of the image domain and high-level semantic descriptions. In this paper, a knowledge-assisted approach for extracting semantics of domain-specific video content is presented. Domain knowledge considers both low-level features (color, motion, shape) and spatial behavior (topological and directional information). During the preprocessing step, a set of over-segmented homogenous atom-regions is generated and their low-level and spatial descriptions are extracted. A genetic algorithm is then applied in order to find the optimal interpretation according to a specific domain conceptualization. The proposed approach was tested on the formula one, tennis and beach vacations domains showing promising results.
Confocal microscopy segmentation using active contour based on alpha(α)-divergence

This paper describes a novel method for active contour segmentation based on foreground/background alpha-divergence histogram distance measure. In recent years a number of variational segmentation techniques have been proposed for a region based active contour segmentation utilising different distance measures between probability density functions (PDFs) describing foreground and background regions. The most common techniques use χ2, Hellinger/Bhattacharya distances or Kullback-Leibler divergence. In this paper, it is proposed to generalize these methods by using the alpha-divergences distance function. This distance function depending on the selected value of its parameter encompasses mentioned above classical distances. The paper defines a partial differential equation, associated with alpha-divergence variational criterion, that governs the iterative deformations of the active contour. The experimental results on a synthetic data demonstrate that the proposed method outperforms previously proposed histogram based methods in terms of segmentation accuracy and robustness with respect to type and level of noise. The potential of the proposed technique for segmentation of cellular structures in fluorescence confocal microscopy data is also illustrated.
An interactive video content-based retrieval system

The actual generation of video search engines offers low-level abstractions of the data while users seek for high-level semantics. The main challenge in video retrieval remains bridging the semantic gap. Thus, the effectiveness of video retrieval is based on the result of the interaction between query selection and a goal-oriented human user. The system exploits the human capability for rapidly scanning imagery augmenting it with an active learning loop, which tries to always present the most relevant material based on the current information. We describe in this paper, a machine learning system for interactive video retrieval. The core of this system is a kernel-based SVM classifier. The video retrieval uses the core as an active learning classifier. We perform an experiment against the 2005 NIST TRECVID benchmark in the high-level task.
Development of Alzheimer's Disease Recognition using Semiautomatic Analysis of Statistical Parameters based on Frequency Characteristics of Medical Images

The paper presents an effective algorithm to analyze MR-images in order to recognize Alzheimer's disease (AD) which appeared in patient's brain. The features of interest are categorized in features of the spatial domain (FSD's) and Features of the frequency domain (FFD's) which are based on the first four statistic moments of the wavelet transform. Extracted features have been classified by a multi-layer perceptron artificial neural network (ANN). Before ANN, the number of features is reduced from 44 to 12 to optimize and eliminate any correlation between them. The contribution of this paper is to demonstrate that by using the wavelet transform number of features needed for AD diagnosis has been reduced in comparison with the previous work. We achieved 79% and 100% accuracy among test set and training set respectively, including 93 MR-images.
Overlapped cells separation algorithm based on morphological system using distance minimums in microscopic images

It is important to distinguish overlapped cell for tracking and segmentation biological cells from images. In this research, a novel or comprehensive method is provided with morphological features of cell and minimum distance for overlapped cells separation (OCS). it's necessary to say that this algorithm is not based on type and number of Overlapped cells. In this presented method based on a distance chart. At first, every cell or rectangle of cell is separated of the original image. Also, multiplex cell is distinguished by using minimum perimeter polygon (MPP) method. Distance chart is achieved on the base of morphological procedure and Computing of cell boundary distance to Cell middle axis in different epochs. By achieving the absolute minimum distance chart, candidate cutting points is detected and separated. Experimental results show the offered method is very effective and the work shows great assistance for the cell Cytoplasm segmentation. At the end, recognition accuracy and 97.4% separation for cells is achieved by this method.
Online undersampled dynamic MRI reconstruction using mutual information

We propose an algorithm based on mutual information to address the problem of online reconstruction of dynamic MRI from partial k-space measurements. Most of previous compressed sensing (CS) based methods successfully leverage sparsity constraint for offline reconstruction of MR images, yet they are not used in online applications due to their complexities. In this paper, we formulate the reconstruction as a constraint optimization problem and try to maximize the mutual information between the current and the previous time frames. Conjugate gradient method is used to solve the optimization problem. Using Cartesian mask to undersample k-space measurements, the proposed method reduces reconstruction error from 3.41% in ModCS, 1.57% in ModCS_Res and 1.16% in CaNNM to 0.61% on average per frame. Moreover, fast reconstruction of images at the rate of 2 to 10 frames per second makes our method a good alternative for current CS based methods in online dynamic MRI applications.
Nonrigid registration of breast MR images using residual complexity similarity measure

Elimination of motion artifact in breast MR images is a significant issue in pre-processing step before utilizing images for diagnostic applications. Breast MR Images are affected by slow varying intensity distortions as a result of contrast agent enhancement. Thus a nonrigid registration algorithm considering this effect is needed. Traditional similarity measures such as sum of squared differences and cross correlation, ignore the mentioned distortion. Therefore, efficient registration is not obtained. Residual complexity is a similarity measure that considers spatially varying intensity distortions by maximizing sparseness of the residual image. In this research, the results obtained by applying nonrigid registration based on residual complexity, sum of squared differences and cross correlation similarity measures are demonstrated which show more robustness and accuracy of RC comparing with other similarity measures for breast MR images.
Manifold learning based registration algorithms applied to multimodal images

Manifold learning algorithms are proposed to be used in image processing based on their ability in preserving data structures while reducing the dimension and the exposure of data structure in lower dimension. Multi-modal images have the same structure and can be registered together as monomodal images if only structural information is shown. As a result, manifold learning is able to transform multi-modal images to mono-modal ones and subsequently do the registration using mono-modal methods. Based on this application, in this paper novel similarity measures are proposed for multi-modal images in which Laplacian eigenmaps are employed as manifold learning algorithm and are tested against rigid registration of PET/MR images. Results show the feasibility of using manifold learning as a way of calculating the similarity between multimodal images.
Automatic landmark extraction from image data using modified growing neural gas network

A new method for automatic landmark extraction from MR brain images is presented. In this method, landmark extraction is accomplished by modifying growing neural gas (GNG), which is a neural-network-based cluster-seeking algorithm. Using modified GNG (MGNG) corresponding dominant points of contours extracted from two corresponding images are found. These contours are borders of segmented anatomical regions from brain images. The presented method is compared to: 1) the node splitting-merging Kohonen model and 2) the Teh-Chin algorithm (a well-known approach for dominant points extraction of ordered curves). It is shown that the proposed algorithm has lower distortion error, ability of extracting landmarks from two corresponding curves simultaneously, and also generates the best match according to five medical experts.
Clustering method for fMRI activation detection using optimal number of clusters

In this study, clustering based method for activation detection in functional magnetic resonance imaging (fMRI) is employed. Moreover, some features are obtained by fitting two models namely FIR filter and Gamma function, to hemodynamic response function (HRF). After applying clustering methods (that require number of clusters as an input) to feature space, our simulations show that number of clusters can affect activation detection significantly. Therefore a newly proposed clustering algorithm namely evolving neural gas (ENG) that gives optimal number of clusters is exploited. In addition to ENG, the result of four clustering algorithms namely k-means, fuzzy C-means, neural gas, and clara in different number of clusters are evaluated. The results show that the best activation detection is taken place using obtained optimal number of clusters.
Comparison of classification and dimensionality reduction methods used in fMRI decoding

In the last few years there has been growing interest in the use of functional Magnetic Resonance Imaging (fMRI) for brain mapping. To decode brain patterns in fMRI data, we need reliable and accurate classifiers. Towards this goal, we compared performance of eleven popular pattern recognition methods. Before performing pattern recognition, applying the dimensionality reduction methods can improve the classification performance; therefore, seven methods in region of interest (RDI) have been compared to answer the following question: which dimensionality reduction procedure performs best? In both tasks, in addition to measuring prediction accuracy, we estimated standard deviation of accuracies to realize more reliable methods. According to all results, we suggest using support vector machines with linear kernel (C-SVM and v-SVM), or random forest classifier on low dimensional subsets, which is prepared by Active or maxDis feature selection method to classify brain activity patterns more efficiently.
Fuzzy local binary patterns: A comparison between Min-Max and Dot-Sum operators in the application of facial expression recognition

The Local Binary Patterns (LBP) feature extraction method is a theoretically and computationally simple and efficient methodology for texture analysis. The LBP operator is used in many applications such as facial expression recognition and face recognition. The original LBP is based on hard thresholding the neighborhood of each pixel, which makes texture representation sensitive to noise. In addition, LBP cannot distinguish between a strong and a weak pattern. In order to enhance the LBP approach, Fuzzy Local Binary Patterns (FLBP) is proposed. In FLBP, any neighborhood does not represented only by one code, but, it is represented by all existing codes with different degrees. In FLBP, any fuzzy Intersection and Union operators may be used. In this study, the following operators are applied and their results are compared together: Dot-Sum, Min-Max and normalized Min-Max. Based on the extensive experiments, the fuzzy Min-Max operators are more useful and can improve the accuracy in the application of Facial Expression Recognition (FER) about 4% (i.e., form 82.98% to 86.88%).
Biometric Identification through Hand Geometry

A new approach for person identification based on hand geometry is presented. After preprocessing hand features are extracted from a photograph taken while user has placed his/her hand (either left or right) on the platform of a document scanner with no limits or fixation. Different pattern recognition techniques like Gaussian mixture modeling (GMM), radial basis function neural networks (RBF), multilayer perceptron (MLP), k-nearest neighbor (k-NN), Bayes method and Mahalanobis/Hamming distance have been used in classification section. Experimental results show a rate of success above 90%
fMRI activation detection by obtaining BOLD response of extracted balloon parameters with Particle Swarm Optimization

Functional magnetic resonance imaging (fMRI) has immense worth in neuroimaging. The basic techniques used in fMRI data analysis were founded on a linear time-invariant system depicted with the impulse response function (HDR). However, there are evidences which accept nonlinear relations between the measured BOLD response and the task, especially in the case of rapid stimulation. Physiological models are replaced with previous impulse response function and use information based on physiological concept. In this paper, balloon model parameters as a physiological model were extracted using particle swarm optimization and then difference between obtained BOLD response from these parameters and measured signal from fMRI, were used to detect the active region.
Image Registration Based on Low Rank Matrix: Rank-Regularized SSD

Similarity measure is a main core of image registration algorithms. Spatially varying intensity distortion is an important challenge, which affects the performance of similarity measures. Correlation among the pixels is the main characteristic of this distortion. Similarity measures such as sum-of-squared-differences (SSD) and mutual information ignore this correlation; hence, perfect registration cannot be achieved in the presence of this distortion. In this paper, we model this correlation with the aid of the low rank matrix theory. Based on this model, we compensate this distortion analytically and introduce rank-regularized SSD (RRSSD). This new similarity measure is a modified SSD based on singular values of difference image in mono-modal imaging. In fact, image registration and distortion correction are performed simultaneously in the proposed model. Based on our experiments, the RRSSD similarity measure achieves clinically acceptable registration results, and outperforms other state-of-the-art similarity measures, such as the well-known method of residual complexity.
Application of independent component analysis for activation detection in functional magnetic resonance imaging (FMRI) data

In this extended summary, our aim is analyzing functional magnetic resonance imaging (fMRI) data by independent component analysis (ICA) in order to find regions of brain which were activated by neural activity in human brain. We employ the minimum description length (MDL) criterion to reduce the dimension of the data and estimate the number of components, which makes ICA work more efficiently. We also use a simple oscillating index method to select automatically the components of interest. MDL and oscillating index criteria have not already been used in applying ICA for analyzing fMRI data. In order to investigate the advantage of using MDL and oscillating index, we perform some experiments for both simulated and experimental fMRI dataset and show the results. In order to justify the performance, receiver operating characteristic (ROC) curve have been drawn.
Medical images stabilization using sparse-induced similarity measure

Medical image stabilization has been widely used for clinical imaging modalities. Registration is a conspicuous stage for stabilizing dynamic medical images. Some of regular methods are sensitive to bias field distortion. Sparse-induced similarity measure (SISM) is a robust registering method against spatially-varying intensity distortions which is not evitable on clinical imaging instruments. This paper presents a method for registering medical images to average of captured images using SISM method to avoid spatially-varying intensity distortions like Bias field. Proposed method is compared with SSD and MI similarity measure based registrations. Results show enhancement in stabilizing medical dynamic images with SISM method.