Adam C. Winstanley

Invariant optimal feature selection: A distance discriminant and feature ranking based solution

The goal of feature selection is to find the optimal subset consisting of m features chosen from the total n features. One critical problem for many feature selection methods is that an exhaustive search strategy has to be applied to seek the best subset among all the possible nm feature subsets, which usually results in a considerably high computational complexity. The alternative suboptimal feature selection methods provide more practical solutions in terms of computational complexity but they cannot promise that the finally selected feature subset is globally optimal. We propose a new feature selection algorithm based on a distance discriminant (FSDD), which not only solves the problem of the high computational costs but also overcomes the drawbacks of the suboptimal methods. The proposed method is able to find the optimal feature subset without exhaustive search or Branch and Bound algorithm. The most difficult problem for optimal feature selection, the search problem, is converted into a feature ranking problem following rigorous theoretical proof such that the computational complexity can be greatly reduced. The proposed method is invariant to the linear transformation of data when a diagonal transformation matrix is applied. FSDD was compared with ReliefF and mrmrMID based on mutual information on 8 data sets. The experiment results show that FSDD outperforms the other two methods and is highly efficient.

An evolutionary algorithm for multicriteria path optimization problems

For many years researchers and decision makers (DMs) faced with multicriteria shortest path problems (MSPPs) have resorted to reductions to the classical shortest path problem (SPP) by means of weighted linear combinations of the criteria. Algorithmic and approximation schemes are available to solve MSPPs but these approaches often display complexities prohibitive to their implementation on real‐world applications. This paper describes the development of an Evolutionary Algorithm (EA) approach to MSPPs on networks with multiple independent criteria. The EA approach is shown to sufficiently explore the underlying network space, generate large candidate path sets, and evolve high quality approximations to the optimal MSPP solution(s). Opportunities for early termination of the EA in time‐critical applications are also offered. Among the issues for further work is the integration of the EA as a tool within a GIS for path optimization.

Segmentation performance evaluation for object-based remotely sensed image analysis

The initial step in most object-based classification methodologies is the application of a segmentation algorithm to define objects. Modelling the human visual process of object segmentation is a challenging task. Many theories in cognitive psychology propose that the human visual system (HVS) initially segments scenes into areas of uniform visual properties or primitive objects. If an accurate primitive-object segmentation algorithm is ever to be realized, a procedure must be in place to evaluate potential solutions. The most commonly used strategy to evaluate segmentation quality is a comparison against ground truth captured by human interpretation. A cognitive experiment reveals that ground truth captured in such a manner is at a larger scale than the desired primitive-object scale. To overcome this difficulty we consider the possibility of evaluating segmentation quality in an unsupervised manner without ground truth. Two requirements for any method which attempts to perform segmentation evaluation in such a manner are proposed, and the importance of these is illustrated by the poor performance of a metric which fails to meet them both. A novel metric, known as the spatial unsupervised (SU) metric, which meets both the requirements is proposed. Results demonstrate the SU metric to be a more reliable metric of segmentation quality compared to existing methods.

Using moment invariants for classifying shapes on large-scale maps

Automated feature extraction and object recognition are large research areas in the field of image processing and computer vision. Recognition is largely based on the matching of descriptions of shapes. Numerous shape description techniques have been developed, such as scalar features (dimension, area, number of corners etc.), Fourier descriptors and moment invariants. These techniques numerically describe shapes independent of translation, scale and rotation and can be easily applied to topographical data. The applicability of the moment invariants technique to classify objects on large-scale maps is described. From the test data used, moments are fairly reliable at distinguishing certain classes of topographic object. However, their effectiveness will increase when fused with the results of other techniques.

A general framework for the evaluation of symbol recognition methods

Performance evaluation is receiving increasing interest in graphics recognition. In this paper, we discuss some questions regarding the definition of a general framework for evaluation of symbol recognition methods. The discussion is centered on three key elements in performance evaluation: test data, evaluation metrics and protocols of evaluation. As a result of this discussion we state some general principles to be taken into account for the definition of such a framework. Finally, we describe the application of this framework to the organization of the first contest on symbol recognition in GREC’03, along with the results obtained by the participants.

Spatial video and GIS

GIS elemental unit representations of spatial data are often defined in terms of points, lines and areas. However, another type of spatial data that is becoming frequently captured, but as yet is largely ignored in GIS, is that of video. While digital video recording is a commonly encountered medium in modern society and encompasses many forms, from simple personal camcorders through to sophisticated survey and surveillance systems, its geographical representation in a GIS has not been fully examined or realised. In the majority of cases the video footage is usually captured while the device and/or the objects being viewed are in motion. What is of particular interest is when video streams can be, or have been, associated with spatial data such as location and orientation to create geographically referenced videographic data, which, for simplicity, will be defined as spatial video. Fundamentally, the nature of video is to record space, so when spatial properties can be accurately acquired and associated with this footage, an important geographical element can be considered for integration and analysis within a GIS. Existing spatial video systems, both commercial and research, are predominantly used in survey or LBS roles and are usually bespoke and application specific. These systems do not model spatial video to any recognised standards that is generalised to be both data and platform independent. They do not support GIS integration and/or analysis from a purely spatial content perspective. A video-image/remote-sensing centric approach prevails where usage options range from simple visualisation interfaces to interactive computer vision systems. What has been largely overlooked is a spatial approach where the inherent geographical extent recorded in each video frame can be modelled and used in a geo-spatial analysis context. While this modelling approach has not been fully realised, it does exist in a GIS form based on Open Geospatial Consortium standards, where the spatial context of video is defined in a structure called a ViewCone. However, a ViewCone only defines a 2D model of the geographical extent of each frame and is restricted to a three-or-five sided polygon representation. Thus, this article examines the potential of modelling spatial video through the use of elemental data types within GIS; gives some examples of using this approach; describes some problems in using spatial video within GIS; and then demonstrates how these problems are being solved. This is done in three stages: Firstly, a detailed overview of spatial video in its current GIS role is provided – this is achieved through a complete introduction to the distinct elements of spatial video followed by a review of its use in both commercial and academic application areas. Secondly, a brief theoretical overview of an alternative GIS-constrained ViewCone data structure is given that defines a more flexible spatial video model for both 2D and 3D GIS analysis and visualisation. Thirdly, a selective sample of results is presented based on an implementation of this approach being applied to a constrained spatial video data source in a specific study area.

Campus Guidance System for International Conferences Based on OpenStreetMap

We present a web-based, multi-lingual, campus guidance system with emphasis on pedestrian navigation aimed at providing support for delegates attending International Conferences at the National University of Ireland Maynooth (NUIM) campus. A special campus guidance system could improve the logistics of the conference and potentially attract more delegates to the conference. The Cloudmade Web Map Lite API which uses OpenStreetMap has been used for creating this interface. The system generates shortest pedestrian paths using both outdoor pavements and indoor corridors between various buildings and points of interests (POI). For visual assistance in pedestrian navigation geotagged images are used along the path at certain points in the route, such as road intersections, when the user needs to get their orientation correct. The interface is currently available in both English and Chinese language.

Haptic-GIS: exploring the possibilities

Haptic technology, or haptics, is a tactile feedback technology that takes advantage of our sense of touch by applying forces, vibrations, and/or motions to the user through a device. Haptic enabled devices have recently gained much publicity in the computer games industry due to their ability to provide a more immersive experience. The use of haptic in the context of GIS and navigation assistance has not previously been considered. We present an overview of Haptic technologies and provide a commentary on how GIS and haptics may crossover and integrate. To demonstrate the potential of haptics for navigation assistance a simple case-study of using haptic feedback as a navigational assistant in pedestrian route planning software is presented. This case-study uses the OpenStreetMap(OSM) database and Cloudmade routing API.

Pedestrian navigation using the sense of touch

Abstract Haptics is a feedback technology that takes advantage of the human sense of touch by applying forces, vibrations, and/or motions to a haptic-enabled user device such as a mobile phone. Historically, human–computer interaction has been visual, data, or images on a screen. Haptic feedback can be an important modality in Mobile Location-Based Services like – knowledge discovery, pedestrian navigation and notification systems. In this paper we describe a methodology for the implementation of haptics in four distinct prototypes for pedestrian navigation. Prototypes are classified based on the user’s navigation guidance requirements, the user type (based on spatial skills), and overall system complexity. Here haptics is used to convey location, orientation, and distance information to users using pedestrian navigation applications. Initial user trials have elicited positive responses from the users who see benefit in being provided with a “heads up” approach to mobile navigation. We also tested the spatial ability of the user to navigate using haptics and landmark images based navigation. This was followed by a test of memory recall about the area. Users were able to successfully navigate from a given origin to a Destination Point without the use of a visual interface like a map. Results show the users of haptic feedback for navigation prepared better maps (better memory recall) of the region as compared to the users of landmark images based navigation.

Planar and non-planar topologically consistent vector map simplification

This article contains a mathematical analysis of strategies for determining topological consistency of vector map simplifications. Such techniques exploit assumptions that can be made regarding the similarity of corresponding objects in successive simplifications. We propose that all topological relationships may be classified as planar or non-planar. A formal analysis of techniques for determining topological consistency of a simplification in terms of such relationships is presented. For each technique we analyse any corresponding constraints that are imposed. This provides a unified understanding of the benefits and limitations of individual techniques and the relationships that exist between techniques. Subsequently, a new strategy for determining the topological consistency of a simplification is proposed. This technique integrates the benefits all methods studied to provide a solution which is subject to less constraints. The effectiveness of this approach is demonstrated through fusion with an existing simplification technique resulting in simplifications that have equal topology and similar shaped features to the original map.