nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised automatic attribute discovery method via multi-graph clustering

Recently, various automated attribute discovery methods have been developed to discover useful visual attributes from a given set of images. Despite the progress made, most methods consider the supervised scenario which assumes the existence of labelled data. Recent results suggest that it is possible to discover attributes from a set of unlabelled data. In this work, we propose a novel unsupervised attribute discovery method utilising multi-graph approach that preserves both local neighbourhood structure as well as class separability. Whilst, the local neighbourhood structure is preserved by considering multiple similarity graphs, the class separability is achieved by incorporating the traditional clustering objective. For evaluation, we first investigate the performance of the proposed approach to address a clustering task. Then we apply our proposed method to automatically discover visual attributes and compare with various automatic attribute discovery and hashing methods. The results show that our proposed method is able to improve the performance in the clustering task. Furthermore, when evaluated using the recent meaningfulness metric, the proposed method outperforms the other unsupervised attribute discovery methods.
Real-Time Face Detection and Tracking for High Resolution Smart Camera System

Smart Cameras are becoming more popular in Intelligent Surveillance Systems area. Recognizing faces in a crowd in real-time is a key features which would significantly enhance Intelligent Surveillance Systems. Using a high resolution smart camera as a tool to extract faces that are suitable for face recognition would greatly reduce the computational load on the main processing unit. This processing unit would not be overloaded by the demands of the high data rates required for high resolution video and could be designed solely for face recognition. In this paper we report on a multiple-stage face detection and tracking system that is designed for implementation on the NICTA high resolution (5 MP) smart camera.
Measurement Function Design for Visual Tracking Applications

Extracting human postural information from video sequences has proved a difficult research question. The most successful approaches to date have been based on particle filtering, whereby the underlying probability distribution is approximated by a set of particles. The shape of the underlying observational probability distribution plays a significant role in determining the success, both accuracy and efficiency, of any visual tracker. In this paper we compare approaches used by other authors and present a cost path approach which is commonly used in image segmentation problems, however is currently not widely used in tracking applications
Homogenized Virtual Support Vector Machines

In many domains, reliable a priori knowledge exists that may be used to improve classifier performance. For example in handwritten digit recognition, such a priori knowledge may include classification invariance with respect to image translations and rotations. In this paper, we present a new generalisation of the Support Vector Machine (SVM) that aims to better incorporate this knowledge. The method is an extension of the Virtual SVM, and penalises an approximation of the variance of the decision function across each grouped set of "virtual examples", thus utilising the fact that these groups should ideally be assigned similar class membership probabilities. The method is shown to be an efficient approximation of the invariant SVM of Chapelle and Sch¨olkopf, with the advantage that it can be solved by trivial modification to standard SVM optimization packages and negligible increase in computational complexity when compared with the Virtual SVM. The efficacy of the method is demonstrated on a simple problem.
To face or not to face: Towards reducing false positive of face detection

We tackle the problem of reducing the false positive rate of face detectors by applying a classifier after the detection step. We first define and study this post classification problem. To this end, we first consider the multiple-stage cascade structure which is the most common face detection architecture. Here, each cascade stage aims to solve a binary classification problem, denoted the Face/non-Face (FnF) problem. In this context, the post classification problem can be considered as the most challenging FnF problem, or the Hard FnF (HFnF) problem. To study the HFnF problem, we propose HFnF datasets derived from the recent face detection datasets. A baseline method utilizing the GIST features and Support Vector Machine (SVM) classifier is also proposed. In our evaluation, we found that it is possible to further improve the face detection performance by addressing the HFnF problem.
Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences

This paper introduces sparse coding and dictionary learning for symmetric positive definite (SPD) matrices, which are often used in machine learning, computer vision, and related areas. Unlike traditional sparse coding schemes that work in vector spaces, in this paper, we discuss how SPD matrices can be described by sparse combination of dictionary atoms, where the atoms are also SPD matrices. We propose to seek sparse coding by embedding the space of SPD matrices into the Hilbert spaces through two types of the Bregman matrix divergences. This not only leads to an efficient way of performing sparse coding but also an online and iterative scheme for dictionary learning. We apply the proposed methods to several computer vision tasks where images are represented by region covariance matrices. Our proposed algorithms outperform state-of-the-art methods on a wide range of classification tasks, including face recognition, action recognition, material classification, and texture categorization.
An Automated Face Recognition System for Intelligence Surveillance: Smart Camera Recognizing Faces in the Crowd

Smart cameras are rapidly finding their way into intelligent surveillance systems. Recognizing faces in the crowd in real-time is one of the key features that will significantly enhance intelligent surveillance systems. The main challenge is the fact that the high volumes of data generated by high-resolution sensors make it computationally impossible for mainstream computers to process. In our proposed technique, the smart camera extracts all the faces from the full-resolution frame and sends the pixel information from these face areas to the main processing unit as a auxiliary video stream - potentially achieving massive data rate reduction. Face recognition software running on the main processing unit then performs the required pattern recognition.
Unsupervised Domain Adaptation by Domain Invariant Projection

Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be directly suitable for such a comparison, since some of the features may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Domain Invariant Projection approach: An unsupervised domain adaptation method that overcomes this issue by extracting the information that is invariant across the source and target domains. More specifically, we learn a projection of the data to a low-dimensional latent space where the distance between the empirical distributions of the source and target examples is minimized. We demonstrate the effectiveness of our approach on the task of visual object recognition and show that it outperforms state-of-the-art methods on a standard domain adaptation benchmark dataset.
An efficient background estimation algorithm for embedded smart cameras

Segmentation of foreground objects of interest from an image sequence is an important task in most smart cameras. Background subtraction is a popular and efficient technique used for segmentation. The method assumes that a background model of the scene under analysis is known. However, in many practical circumstances it is unavailable and needs to be estimated from cluttered image sequences. With embedded systems as the target platform, in this paper we propose a sequential technique for background estimation in such conditions, with low computational and memory requirements. The first stage is somewhat similar to that of the recently proposed agglomerative clustering background estimation method, where image sequences are analysed on a block by block basis. For each block location a representative set is maintained which contains distinct blocks obtained along its temporal line. The novelties lie in iteratively filling in background areas by selecting the most appropriate candidate blocks according to the combined frequency responses of extended versions of the candidate block and its neighbourhood. It is assumed that the most appropriate block results in the smoothest response, indirectly enforcing the spatial continuity of structures within a scene. Experiments on real-life surveillance videos demonstrate the advantages of the proposed method.
Self Occlusions and Graph Based Edge Measurement Schemes for Visual Tracking Applications

The success of visual tracking systems is highly dependent upon the effectiveness of the measurement function used to evaluate the likelihood of a hypothesized object state. Generative tracking algorithms attempt to find the global and other local maxima of these measurement functions. As such, designing measurement functions which have a small number of local maxima is highly desirable. Edge based measurements are an integral component of most measurement functions. Graph based methods are commonly used for image segmentation, and more recently have been applied to visual tracking problems. When self occlusions are present, it is necessary to find the shortest path across a graph when the weights of some graph vertices are unknown. In this paper, treatments are given for handling object self occlusions in graph based edge measurement methods. Experiments are performed to test the effect that each of these treatments has on the accuracy and number of modes in the observational likelihood.
A multiple covariance approach for cell detection of Gram-stained smears images

Microscope examination of Gram stained clinical specimens is used for aiding the diagnosis of patients with infectious diseases. In high volume pathology laboratories, this manual microscopy examination is considered time consuming and labour intensive. Unfortunately, despite the great benefits offered from the application of Computer Aided Diagnosis (CAD) systems, to our knowledge, the highest automation stage for Gram stained slide analysis is only at the pre-analytical process. This paper takes the first steps towards the application of computer vision to direct smear, Gram stained images. To that end, we present a novel Gram stain image dataset. In addition, we also propose a multiple covariance approach for leukocyte and epithelial cell detection in Gram stain images. Each covariance matrix represents a particular image region characterising the cell's deformed structure. As covariance matrices form points on an Symmetric Positive Definite (SPD) manifold, the traditional Euclidean-based analysis cannot be used. As such, we first map the manifold points into the Reproducing Kernel Hilbert Space (RKHS). The analysis is done via a novel kernel similarity function that allows comparison between sets of covariance matrices. The proposed approach is contrasted, in the proposed dataset, with two recent state of the art methods in pedestrian detection: Histogram Of Gradient (HOG) and the traditional single covariance matrix approach. We found that the proposed approach outperformed both of these methods.
Robust Foreground Object Segmentation via Adaptive Region-Based Background Modelling

We propose a region-based foreground object segmentation method capable of dealing with image sequences containing noise, illumination variations and dynamic backgrounds (as often present in outdoor environments). The method utilises contextual spatial information through analysing each frame on an overlapping block by-block basis and obtaining a low-dimensional texture descriptor for each block. Each descriptor is passed through an adaptive multi-stage classifier, comprised of a likelihood evaluation, an illumination invariant measure, and a temporal correlation check. The overlapping of blocks not only ensures smooth contours of the foreground objects but also effectively minimises the number of false positives in the generated foreground masks. The parameter settings are robust against wide variety of sequences and post-processing of foreground masks is not required. Experiments on the challenging I2R dataset show that the proposed method obtains considerably better results (both qualitatively and quantitatively) than methods based on Gaussian mixture models (GMMs), feature histograms, and normalised vector distances. On average, the proposed method achieves 36% more accurate foreground masks than the GMM based method.
Spatio-temporal covariance descriptors for action and gesture recognition

We propose a new action and gesture recognition method based on spatio-temporal covariance descriptors and a weighted Riemannian locality preserving projection approach that takes into account the curved space formed by the descriptors. The weighted projection is then exploited during boosting to create a final multiclass classification algorithm that employs the most useful spatio-temporal regions. We also show how the descriptors can be computed quickly through the use of integral video representations. Experiments on the UCF sport, CK+ facial expression and Cambridge hand gesture datasets indicate superior performance of the proposed method compared to several recent state-of-the-art techniques. The proposed method is robust and does not require additional processing of the videos, such as foreground detection, interest-point detection or tracking.
The statistical performance of some instantaneous frequency estimators

The authors examine the class of smoothed central finite difference (SCFD) instantaneous frequency (IF) estimators which are based on finite differencing of the phase of the analytic signal. These estimators are closely related to IF estimation via the (periodic) first moment, with respect to frequency of discrete time-frequency representations (TFRs) in L. Cohen's (1966) class. The authors determine the distribution of this class of estimators and establish a framework which allows the comparison of several other estimators such as the zero-crossing estimator and one based on linear regression on the signal phase. It is found that the regression IF estimator is biased and exhibits a large threshold for much of the frequency range. By replacing the linear convolution operation in the regression estimator with the appropriate convolution operation for circular data the authors obtain the parabolic SCFD (PSCFD) estimator, which is unbiased and has a frequency-independent variance, yet retains the optimal performance and simplicity of the original estimator.
A wireless mesh sensor network for hazard and safety monitoring at the Port of Brisbane

A wireless sensor network (WSN) was designed and implemented to provide reliable long-term hazard monitoring at the Port of Brisbane, Australia. The proposed system consists of four sensor nodes, a wireless gateway and a central monitoring computer. The sensor nodes are capable of measuring a range of hazardous events along with the time and location of events in maritime environment. Each sensor node is also equipped with a Global Positioning System (GPS) module and a ZigBee module. The monitoring server is a personal computer application server with Internet connectivity. Sensor nodes utilize smart algorithm to save energy and AES-128 encryption to encode data prior to sending the data packet through the wireless ZigBee protocol to gateway. The gateway collects and decrypts the data packets and forwards them to the monitoring computer through wireless connection. A database server running on the monitoring computer stores the captured data for visualization and further analysis. The monitoring server is interfaced to Google Maps to overlay real-time data from the sensor nodes onto map in the correct corresponding locations. The WSN system was successfully deployed and tested at the Port of Brisbane, Queensland,
Role of Spatiotemporal Oriented Energy Features for Robust Visual Tracking in Video Surveillance

We propose an effective approach to take advantage of the rich description provided by Spatiotemporal Oriented Energy features for the purpose of robust tracking. There are two core components in our system. The first one is a compound measure of 'Coherent Motion' and 'Identity Motion Signature' which is introduced based on motion dynamics of the targets. This measure is used for robust optimisation in occluded situations as well as an adaptive template updating scheme. The second component is a state machine which detects various states of the targets based on statistical analysis of their 'Motion Signature'. Empirical evaluations demonstrate improvement in performance of the tracking system along with the role of each component.
Discriminative Non-Linear Stationary Subspace Analysis for Video Classification

Low-dimensional representations are key to the success of many video classification algorithms. However, the commonly-used dimensionality reduction techniques fail to account for the fact that only part of the signal is shared across all the videos in one class. As a consequence, the resulting representations contain instance-specific information, which introduces noise in the classification process. In this paper, we introduce non-linear stationary subspace analysis: a method that overcomes this issue by explicitly separating the stationary parts of the video signal (i.e., the parts shared across all videos in one class), from its non-stationary parts (i.e., the parts specific to individual videos). Our method also encourages the new representation to be discriminative, thus accounting for the underlying classification problem. We demonstrate the effectiveness of our approach on dynamic texture recognition, scene classification and action recognition.
Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture

A robust and efficient anomaly detection technique is proposed, capable of dealing with crowded scenes where traditional tracking based approaches tend to fail. Initial foreground segmentation of the input frames confines the analysis to foreground objects and effectively ignores irrelevant background dynamics. Input frames are split into non-overlapping cells, followed by extracting features based on motion, size and texture from each cell. Each feature type is independently analysed for the presence of an anomaly. Unlike most methods, a refined estimate of object motion is achieved by computing the optical flow of only the foreground pixels. The motion and size features are modelled by an approximated version of kernel density estimation, which is computationally efficient even for large training datasets. Texture features are modelled by an adaptively grown code-book, with the number of entries in the codebook selected in an online fashion. Experiments on the recently published UCSD Anomaly Detection dataset show that the proposed method obtains considerably better results than three recent approaches: MPPCA, social force, and mixture of dynamic textures (MDT). The proposed method is also several orders of magnitude faster than MDT, the next best performing method.
Object tracking via non-Euclidean geometry: A Grassmann approach

A robust visual tracking system requires an object appearance model that is able to handle occlusion, pose, and illumination variations in the video stream. This can be difficult to accomplish when the model is trained using only a single image. In this paper, we first propose a tracking approach based on affine subspaces (constructed from several images) which are able to accommodate the above-mentioned variations. We use affine subspaces not only to represent the object, but also the candidate areas that the object may occupy. We furthermore propose a novel approach to measure affine subspace-to-subspace distance via the use of non-Euclidean geometry of Grassmann manifolds. The tracking problem is then considered as an inference task in a Markov Chain Monte Carlo framework via particle filtering. Quantitative evaluation on challenging video sequences indicates that the proposed approach obtains considerably better performance than several recent state-of-the-art methods such as Tracking-Learning-Detection and MILtrack.
Improved estimation of hidden Markov model parameters from multiple observation sequences

The huge popularity of hidden Markov models (HMMs) in pattern recognition is due to the ability to "learn" model parameters from an observation sequence through Baum-Welch and other re-estimation procedures. In the case of HMM parameter estimation from an ensemble of observation sequences, rather than a single sequence, we require techniques for finding the parameters which maximize the likelihood of the estimated model given the entire set of observation sequences. The importance of this study is that HMMs with parameters estimated from multiple observations are shown to be many orders of magnitude more probable than HMM models learned from any single observation sequence - thus the effectiveness of HMM "learning" is greatly enhanced. In this paper we present techniques that usually find models significantly more likely than Rabiner's well-known method on both seen and unseen sequences.
Square Patch Feature: Faster weak-classifier for robust object detection

This paper presents a novel generic weak classifier for object detection called "Square Patch Feature". The speed and overall performance of a detector utilising Square Patch features in comparison to other weak classifiers shows improvement. Each weak classifier is based on the difference between two or four fixed size square patches in an image. A pre-calculated representation of the image called "patch image" is required to accelerate the weak classifiers computation. The computation requires fewer arithmetic operations and fewer accesses to the main memory in comparison to the well known Viola-Jones Haar-like classifier. In addition to the faster computation, the weak classifier can be extended for in-plane rotation, where each square patch can be rotated to detect in-plane rotated objects. The results of the experiments on the MIT CBCL Face dataset show that a Square Patch Feature classifier is as accurate as the Viola-Jones Haar-like classifier, and when implemented on hardware (i.e. FPGA), it is almost 2 times faster.
Manifold convex hull (MACH): Satisfying a need for SPD

In this paper, we extend the nearest convex hull classifier to Symmetric Positive Definite (SPD) manifolds. SPD manifold features have been shown to have excellent performance in various image/video classification tasks. Unfortunately, SPD manifolds naturally possess non-Euclidean geometry, so existing Euclidean machineries such as the nearest convex hull classifier cannot be used directly. To that end, we propose a novel mathematical framework, named Manifold Convex Hull (MACH), that extends the nearest convex hull classifier to SPD Manifolds. The superior performance of our nearest convex hull framework on SPD manifolds is demonstrated in several computer vision applications including object recognition, pedestrian detection and texture classification.