Locality Preserving Projections for Grassmann manifold

Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used dimensionality reduction algorithm for vector-valued data, aiming to preserve local structure of data in the dimension-reduced space. The strategy is to construct a mapping from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability. The proposed method can be optimized as a basic eigenvalue problem. The performance of our proposed method is assessed on several classification and clustering tasks and the experimental results show its clear advantages over other Grassmann based algorithms.Comment: Accepted by IJCAI 201

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

Identification of Unknown Landscape Types Using CNN Transfer Learning

Unknown image type identification is the problem of identifying unknown types of images from the set of already provided images that are considered to be known, where the known and unknown sets represent different content types. Solving this problem has a lot of security applications such as suspicious object detection during baggage scanning at airport customs, border protection via remote sensing, cancer detection, weather and disaster monitoring, etc. In this thesis, we focus on identification of unknown landscape images. This application has a huge relevance to the context of a smart nation where it can be applied to major national security tasks such as monitoring the borders or the detection of unknown and potentially dangerous landscapes in critical locations. We propose effective semi-supervised novelty detection approaches for the unknown image type identification problem using Convolutional Neural Network (CNN) Transfer Learning. Recently, the CNN Transfer Learning approach has been very successful in various visual recognition tasks especially in cases where large training data is not available. Our main idea is to use pre-trained CNNs (i.e. already trained on large datasets like ImageNet [10]) that are then used to train new models specifically applicable to the landscape image dataset. Features extracted from these domain-specific trained CNN are then used with standard semi-supervised novelty detection algorithms like Gaussian Mixture Model, Isolation Forest, One-class Support Vector Machines (SVM) and Bayesian Gaussian Mixture Models to identify the unknown landscape images. We provide two fine-tuning approaches: supervised and unsupervised. Supervised fine-tuning approach simply uses the the class categories (landscape classes, e.g. airport, stadium, etc.) of the known images dataset. The unsupervised fine tuning approach on the other hand learns the class categories from the known images using the unsupervised clustering-based algorithm. We conducted extensive experiments that prove the effectiveness of our approaches. Our best values of AUROC and average precision scores for the identification problem are 0.96 and 0.94, respectively. In particular, we statistically prove that both fine-tuning methods significantly increase the performance of the identification with respect to the non fine-tuned CNN, and unsupervised and supervised fine tuning approaches are comparable

Parallel visual data restoration on multi-GPGPUs using stencil-reduce pattern

In this paper, a highly effective parallel filter for visual data restoration is presented. The filter is designed following a skeletal approach, using a newly proposed stencil-reduce, and has been implemented by way of the FastFlow parallel programming library. As a result of its high-level design, it is possible to run the filter seamlessly on a multicore machine, on multi-GPGPUs, or on both. The design and implementation of the filter are discussed, and an experimental evaluation is presented