Arnold Wiliem

Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures

A convenient way of analysing Riemannian manifolds is to embed them in Euclidean spaces, with the embedding typically obtained by flattening the manifold via tangent spaces. This general approach is not free of drawbacks. For example, only distances between points to the tangent pole are equal to true geodesic distances. This is restrictive and may lead to inaccurate modelling. Instead of using tangent spaces, we propose embedding into the Reproducing Kernel Hilbert Space by introducing a Riemannian pseudo kernel. We furthermore propose to recast a locality preserving projection technique from Euclidean spaces to Riemannian manifolds, in order to demonstrate the benefits of the embedding. Experiments on several visual classification tasks (gesture recognition, person re-identification and texture classification) show that in comparison to tangent-based processing and state-of-the-art methods (such as tensor canonical correlation analysis), the proposed approach obtains considerable improvements in discrimination accuracy.

Fisher tensors for classifying human epithelial cells

Analyzing and classifying Human Epithelial type 2 (HEp-2) cells using Indirect Immunofluorescence protocol has been the golden standard for detecting connective tissue diseases such as Rheumatoid Arthritis. However, this suffers from numerous shortcomings such as being subjective as well as time and labor intensive. Recently, several studies explore the advantages of artificial systems to automate the process, not only to reduce the test turn-around time but also to deliver more consistent results. In this paper, we extend the conventional bag of word models from Euclidean space to non-Euclidean Riemannian manifolds and utilize them to classify the HEp-2 cells. The main motivation comes from the observation that HEp-2 cells can be efficiently described by symmetric positive definite matrices which lie on a Riemannian manifold. With this motivation, we first discuss an intrinsic bag of Riemannian words model. We then propose Fisher tensors which can in turn encode additional information about the distribution of the signatures in a bag of word model. Experiments on two challenging HEp-2 images datasets, namely ICPRContest and SNPHEp-2 show that the proposed methods obtain notable improvements in discrimination accuracy, in comparison to baseline and several state-of-the-art methods. The proposed framework, while hand-crafted towards cell classification, is a generic framework for object recognition. This is supported by assessing the performance of our proposal on a challenging texture classification task.

Automatic classification of Human Epithelial type 2 cell Indirect Immunofluorescence images using Cell Pyramid Matching

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset.

Classification of Human Epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors

The Anti-Nuclear Antibody (ANA) clinical pathology test is commonly used to identify the existence of various diseases. A hallmark method for identifying the presence of ANAs is the Indirect Immunofluorescence method on Human Epithelial (HEp-2) cells, due to its high sensitivity and the large range of antigens that can be detected. However, the method suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg., speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. In this paper, we propose a cell classification system comprised of a dual-region codebook-based descriptor, combined with the Nearest Convex Hull Classifier. We evaluate the performance of several variants of the descriptor on two publicly available datasets: ICPR HEp-2 cell classification contest dataset and the new SNPHEp-2 dataset. To our knowledge, this is the first time codebook-based descriptors are applied and studied in this domain. Experiments show that the proposed system has consistent high performance and is more robust than two recent CAD systems.

Face Recognition on Consumer Devices: Reflections on Replay Attacks

Widespread deployment of biometric systems supporting consumer transactions is starting to occur. Smart consumer devices, such as tablets and phones, have the potential to act as biometric readers authenticating user transactions. However, the use of these devices in uncontrolled environments is highly susceptible to replay attacks, where these biometric data are captured and replayed at a later time. Current approaches to counter replay attacks in this context are inadequate. In order to show this, we demonstrate a simple replay attack that is 100% effective against a recent state-of-the-art face recognition system; this system was specifically designed to robustly distinguish between live people and spoofing attempts, such as photographs. This paper proposes an approach to counter replay attacks for face recognition on smart consumer devices using a noninvasive challenge and response technique. The image on the screen creates the challenge, and the dynamic reflection from the persons face as they look at the screen forms the response. The sequence of screen images and their associated reflections digitally watermarks the video. By extracting the features from the reflection region, it is possible to determine if the reflection matches the sequence of images that were displayed on the screen. Experiments indicate that the face reflection sequences can be classified under ideal conditions with a high degree of confidence. These encouraging results may pave the way for further studies in the use of video analysis for defeating biometric replay attacks on consumer devices.

Benchmarking human epithelial type 2 interphase cells classification methods on a very large dataset

OBJECTIVE This paper presents benchmarking results of human epithelial type 2 (HEp-2) interphase cell image classification methods on a very large dataset. The indirect immunofluorescence method applied on HEp-2 cells has been the gold standard to identify connective tissue diseases such as systemic lupus erythematosus and Sjögrens syndrome. However, the method suffers from numerous issues such as being subjective, time consuming and labor intensive. This has been the main motivation for the development of various computer-aided diagnosis systems whose main task is to automatically classify a given cell image into one of the predefined classes. METHODS AND MATERIAL The benchmarking was performed in the form of an international competition held in conjunction with the International Conference of Image Processing in 2013: fourteen teams, composed of practitioners and researchers in this area, took part in the initiative. The system developed by each team was trained and tested on a very large HEp-2 cell dataset comprising over 68,000 images of HEp-2 cell. The dataset contains cells with six different staining patterns and two levels of fluorescence intensity. For each method we provide a brief description highlighting the design choices and an in-depth analysis on the benchmarking results. RESULTS The staining pattern recognition accuracy attained by the methods varies between 47.91% and slightly above 83.65%. However, the difference between the top performing method and the seventh ranked method is only 5%. In the paper, we also study the performance achieved by fusing the best methods, finding that a recognition rate of 85.60% is reached when the top seven methods are employed. CONCLUSIONS We found that highest performance is obtained when using a strong classifier (typically a kernelised support vector machine) in conjunction with features extracted from local statistics. Furthermore, the misclassification profiles of the different methods highlight that some staining patterns are intrinsically more difficult to recognize. We also noted that performance is strongly affected by the fluorescence intensity level. Thus, low accuracy is to be expected when analyzing low contrasted images.

Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns

Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (IIF) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset. To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalians vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into “cubic regions”, and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2.

Classifying Anti-nuclear Antibodies HEp-2 Images: A Benchmarking Platform

There has been an ongoing effort in improving reliability and consistency of pathology test results due to their critical role in making an accurate diagnosis. One way to do this is by applying image-based Computer Aided Diagnosis (CAD) systems. This paper proposes a comprehensive benchmarking platform comprising over 1,000 images to evaluate CAD systems for the Anti-Nuclear Antibody (ANA) test via the Indirect Immunofluorescence (IIF) protocol applied on Human Epithelial Type 2 (HEp-2) cells. While prior works in this domain have primarily focussed on classifying individual cell images derived from ANA IIF HEp-2 images, our proposed benchmarking platform goes beyond this by considering the ANA IIF HEp-2 image classification problem. Generally the existing works derive an ANA IIF HEp-2 image label from the dominant pattern of the cell images (we call this approach baseline). In this work, we argue that this approach cannot be used to achieve an acceptable performance, thus, the problem of classifying ANA IIF HEp-2 images (or ANA images in short) is still largely unexplored. To demonstrate that, we propose a simple-yet-effective CAD system which is inspired from the recent success of object bank representation in the object classification domain. We evaluate the proposed system, the baseline and a recent CAD system and show that our proposed system considerably outperforms the others.

Discovering discriminative cell attributes for HEp-2 specimen image classification

Recently, there has been a growing interest in developing Computer Aided Diagnostic (CAD) systems for improving the reliability and consistency of pathology test results. This paper describes a novel CAD system for the Anti-Nuclear Antibody (ANA) test via Indirect Immunofluorescence protocol on Human Epithelial Type 2 (HEp-2) cells. While prior works have primarily focused on classifying cell images extracted from ANA specimen images, this work takes a further step by focussing on the specimen image classification problem itself. Our system is able to efficiently classify specimen images as well as producing meaningful descriptions of ANA pattern class which helps physicians to understand the differences between various ANA patterns. We achieve this goal by designing a specimen-level image descriptor that: (1) is highly discriminative; (2) has small descriptor length and (3) is semantically meaningful at the cell level. In our work, a specimen image descriptor is represented by its overall cell attribute descriptors. As such, we propose two max-margin based learning schemes to discover cell attributes whilst still maintaining the discrimination of the specimen image descriptor. Our learning schemes differ from the existing discriminative attribute learning approaches as they primarily focus on discovering image-level attributes. Comparative evaluations were undertaken to contrast the proposed approach to various state-of-the-art approaches on a novel HEp-2 cell dataset which was specifically proposed for the specimen-level classification. Finally, we showcase the ability of the proposed approach to provide textual descriptions to explain ANA patterns.

Efficient clustering on Riemannian manifolds

Reformulating computer vision problems over Riemannian manifolds has demonstrated superior performance in various computer vision applications. This is because visual data often forms a special structure lying on a lower dimensional space embedded in a higher dimensional space. However, since these manifolds belong to non-Euclidean topological spaces, exploiting their structures is computationally expensive, especially when one considers the clustering analysis of massive amounts of data. To this end, we propose an efficient framework to address the clustering problem on Riemannian manifolds. This framework implements random projections for manifold points via kernel space, which can preserve the geometric structure of the original space, but is computationally efficient. Here, we introduce three methods that follow our framework. We then validate our framework on several computer vision applications by comparing against popular clustering methods on Riemannian manifolds. Experimental results demonstrate that our framework maintains the performance of the clustering whilst massively reducing computational complexity by over two orders of magnitude in some cases. HighlightsWe propose a kernelised random projection framework for clustering manifold points.We present three projection methods conforming to our proposed framework.We contrast our proposal to clustering methods on manifolds in various vision tasks.We show the proposal obtain significant speed up whilst maintaining the performance.We analyse the parameters contributing to the speed up.