Many large-scale systems benefit from an organizational structure to provide for problem decomposition. A pivotal problem solving setting is given by hierarchical control systems familiar from hierarchical task networ...
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Many large-scale systems benefit from an organizational structure to provide for problem decomposition. A pivotal problem solving setting is given by hierarchical control systems familiar from hierarchical task networks. If these structures can be modified autonomously by, e.g., Coalition formation and reconfiguration, adequate decisions on higher levels require a faithful abstracted model of a collective of agents. An illustrative example is found in calculating schedules for a set of power plants organized in a hierarchy of Autonomous Virtual Power Plants. Functional dependencies over the combinatorial domain, such as the joint costs or rates of change of power production, are approximated by repeatedly sampling input-output pairs and substituting the actual functions by piecewise linear functions. However, if the sampled data points are weakly informative, the resulting abstracted high-level optimization introduces severe errors. Furthermore, obtaining additional point labels amounts to solving computationally hard optimization problems. Building on prior work, we propose to apply techniques from active learning to maximize the information gained by each additional point. Our results show that significantly better allocations in terms of cost-efficiency (up to 33.7 % reduction in costs in our case study) can be found with fewer but carefully selected sampling points using Decision Forests.
The enormous growth of image databases calls for new techniques for fast and effective image search that scales with millions of images. Most importantly, the setting requires a compact but also descriptive image sign...
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ISBN:
(纸本)9781479957521
The enormous growth of image databases calls for new techniques for fast and effective image search that scales with millions of images. Most importantly, the setting requires a compact but also descriptive image signature. Recently, the vector of aggregated local descriptors (VLAD) [1] has received much attention in large-scale image retrieval. In this paper we present two modifications for VLAD which improve the retrieval performance of the signature.
In this paper we present a method for aligning shredded document pieces based on outer contours and content-based prior information. Our approach relies on domain-specific knowledge that document pieces must complemen...
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ISBN:
(纸本)9781479947607
In this paper we present a method for aligning shredded document pieces based on outer contours and content-based prior information. Our approach relies on domain-specific knowledge that document pieces must complement each other when aligned correctly. Building on this intuition we propose a variant of MSAC (M-estimator SAmple Consensus) to estimate an hypothesis that recovers the spatial relationship between pairs of pieces. To do so we first approximate their boundaries by polygons from which we define consensus sets between fragments. Each consensus set provides multiple hypotheses for aligning one piece onto the other. An optimal hypothesis is identified by applying a two-stage procedure in which we discard locally inconsistent hypotheses before verifying the remainder for global consistency.
We present a scalable logo recognition technique based on feature bundling. Individual local features are aggregated with features from their spatial neighborhood into bundles. These bundles carry more information abo...
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Feature learning has the aim to take away the hassle of hand-designing features for machine learning tasks. Since the feature design process is tedious and requires a lot of experience, an automated solution is of gre...
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ISBN:
(纸本)9781479941551
Feature learning has the aim to take away the hassle of hand-designing features for machine learning tasks. Since the feature design process is tedious and requires a lot of experience, an automated solution is of great interest. However, an important problem in this field is that usually no objective values are available to fit a feature learning function to. Artificial Neural Networks are a sufficiently flexible tool for function approximation to be able to avoid this problem. We show how the error function of an ANN can be modified such that it works solely with objective distances instead of objective values. We derive the adjusted rules for back propagation through networks with arbitrary depths and include practical considerations that must be taken into account to apply difference based learning successfully. On all three benchmark datasets we use, linear SVMs trained on automatically learned ANN features outperform RBF kernel SVMs trained on the raw data. This can be achieved in a feature space with up to only a tenth of dimensions of the number of original data dimensions. We conclude our work with two experiments on distance based ANN training in two further fields: data visualization and outlier detection.
As new information and communications systems are being equipped with more aggressive capabilities to enable smart surveillance, individuals' private and ethical data is more exposed to potential threats. Conseque...
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As new information and communications systems are being equipped with more aggressive capabilities to enable smart surveillance, individuals' private and ethical data is more exposed to potential threats. Consequently, the attention of researchers and policy makers has become increasingly focused on controlling the emerging threats to privacy. In order to ensure that a surveillance system framework complies with the legal, ethical and privacy requirements of the law, in this paper we present a Surveillance Ontology extending the SKOS foundational ontology. The fundamental principles of privacy-by-design (PbD) demand that the surveillance framework consider data minimization, user control, accountability and data separation. Hence, the objective of this ontology is to translate the high-level linguistic rules into the information that can be processed and used to assess the compliance of the video analysis module with the rules defined.
In a world filled with heightened vandalism and terrorist activities, video surveillance forms an integral part of any incident investigation. While aiming to provide safety and security for the citizens, CCTV cameras...
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ISBN:
(纸本)9781467358064
In a world filled with heightened vandalism and terrorist activities, video surveillance forms an integral part of any incident investigation. While aiming to provide safety and security for the citizens, CCTV cameras are exponentially deployed around metropolitan cities. However, the information thus collected and further processed should comply with the legal and ethical rules defined by the law. The primary ethical issue invoked by surveillance activities in general is that of privacy. In order to ensure that a surveillance system framework complies with the legal, ethical and privacy requirements of the law, in this paper we present a Surveillance Ontology extending the SKOS foundational ontology. The objective of this ontology is to translate the high-level linguistic rules into the information that can be processed and used to assess the compliance of the video analysis module with the rules defined.
In this work we present a feature bundling technique that aggregates individual local features with features from their spatial neighborhood into bundles. The resulting bundles carry more information of the underlying...
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