Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly

Monitoring land use changes using geo-information : possibilities, methods and adapted techniques

Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets

A High-Definition Spatially Explicit Modeling Approach for National Greenhouse Gas Emissions from Industrial Processes: Reducing the Errors and Uncertainties in Global Emission Modeling

Spatially-explicit (gridded) emission inventories (EIs) should allow us to analyse sectoral emissions patterns to estimate potential impacts of emission policies and support decisions on reducing emissions. However, such EIs are often based on simple downscaling of national level emissions estimate and the changes in subnational emissions distributions are not necessarily reflecting the actual changes driven by the local emissions drivers. This article presents a high definition,100m resolution bottom-up inventory of greenhouse gas (GHG) emissions from the industrial processes (fuel combustion activities in energy and manufacturing industry, fugitive emissions, mineral products, chemical industry, metal production, food and drink) that is exemplified on data for Poland. We propose an improved emission disaggregation algorithmthat fully utilizes a collection of activity data available at national/provincial level to the level of individual point and diffused (area) emission sources. To ensure the accuracy of the resulting 100m emission fields, the geospatial data used for mapping emission sources (point source geolocation and land cover classification) were subject to thorough human visual inspection.The resulting 100m emission field even hold cadastres of emissions separately for each industrial emission category, while we start with IPCC-compliant national sectoral GHG estimates that we made using Polish official statistics. We aggregated the resulting emissions to the level of administrative units such as municipalities, districts and provinces. We also compiled cadastres in regular grids and then compared them with EDGAR results. Quantitative analysis of discrepancies between both results revealed quite frequent misallocations of point sources used in the EDGAR compilation that considerably deteriorates high resolution inventories. We also propose a Monte-Carlo method-based uncertainty assessment that yields a detailed estimation of the GHG emission uncertainty in the main categories of the analysed processes. We found that the above mentioned geographical coordinates and patterns used for emission disaggregation have the greatest impact on overall uncertainty of GHG inventoriesfrom the industrial processes

Knowledge-based systems and geological survey

This personal and pragmatic review of the philosophy underpinning methods of geological surveying suggests that important influences of information technology have yet to make their impact. Early approaches took existing systems as metaphors, retaining the separation of maps, map explanations and information archives, organised around map sheets of fixed boundaries, scale and content. But system design should look ahead: a computer-based knowledge system for the same purpose can be built around hierarchies of spatial objects and their relationships, with maps as one means of visualisation, and information types linked as hypermedia and integrated in mark-up languages. The system framework and ontology, derived from the general geoscience model, could support consistent representation of the underlying concepts and maintain reference information on object classes and their behaviour. Models of processes and historical configurations could clarify the reasoning at any level of object detail and introduce new concepts such as complex systems. The up-to-date interpretation might centre on spatial models, constructed with explicit geological reasoning and evaluation of uncertainties. Assuming (at a future time) full computer support, the field survey results could be collected in real time as a multimedia stream, hyperlinked to and interacting with the other parts of the system as appropriate. Throughout, the knowledge is seen as human knowledge, with interactive computer support for recording and storing the information and processing it by such means as interpolating, correlating, browsing, selecting, retrieving, manipulating, calculating, analysing, generalising, filtering, visualising and delivering the results. Responsibilities may have to be reconsidered for various aspects of the system, such as: field surveying; spatial models and interpretation; geological processes, past configurations and reasoning; standard setting, system framework and ontology maintenance; training; storage, preservation, and dissemination of digital records

Data Mining for Shopping Centres – Customer Knowledge-Management Framework

Shopping centers are an important part of the UK economy and have been the subject of considerable research. Relying on complex interdependencies between shoppers, retailers and owners, shopping centers are ideal for knowledge management study. Nevertheless, although retailers have been in the forefront of data mining, little has been written on Customer Knowledge Management for shopping centers. In this chapter, the authors aim to demonstrate the possibilities and draw attention to the possible implications of improving customer satisfaction. Aspects of customer knowledge management for shopping centers are considered using analogies drawn from an exploratory questionnaire survey. The objectives of a Customer Knowledge Management system could include increasing rental incomes and bringing new life back into shopping centers and towns

Learning Behavioural Context

The original publication is available at www.springerlink.co