Interactive machine acquisition of a fuzzy spatial relation

Robinson, Vincent Β.

doi:10.1016/0098-3004(90)90008-h

Cited by 64 publications

(23 citation statements)

References 8 publications

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“…A large number of prior works used fuzzy logic and qualitative techniques to deal with spatial proximity because it has inherent fuzziness (Robinson, 1990). While reasoning with proximity, human beings may also consider metric distances and other parameters called contextual information.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Spatial Proximity System Using Neurofuzzy Classifiers and Contextual Information

Barouni

Moulin

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In this paper, we propose a novel approach to reason with spatial proximity. The approach is based on contextual information and uses a neurofuzzy classifier to handle the uncertainty aspect of proximity. Neurofuzzy systems are a combination of neural networks and fuzzy systems and incorporate the advantages of both techniques. Although fuzzy systems are focused on knowledge representation, they do not allow the estimation of membership functions. Conversely, neuronal networks use powerful learning techniques but they are not able to explain how results are obtained. Neurofuzzy systems benefit from both techniques by using training data to generate membership functions and by using fuzzy rules to represent expert knowledge. Moreover, contextual information is collected from a knowledge base. The complete solution that we propose is integrated in a GIS, enhancing it with proximity reasoning. From an application perspective, the proposed approach was used in the telecommunication domain and particularly in fiber optic monitoring systems. In such systems, a user needs to qualify the distance between a fiber break and the surrounding objects of the environment to optimize the assignment of emergency crews. The neurofuzzy classifier has been used to compute the membership function parameters of the contextual information inputs using a training data set and fuzzy rules.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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Section: Related Workmentioning

confidence: 99%

An Intelligent Spatial Proximity System Using Neurofuzzy Classifiers and Contextual Information

Barouni

Moulin

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Of particular relevance to our task of generating locational expressions in specific geographic (as opposed to "table top") contexts are a number of empirical, human-subject studies of the use of vague spatial language concepts that have been concerned with the possibility of fitting models to the experimental data. For example, Robinson conducted studies to acquire fuzzy membership functions to represent the concept of nearness [20] [21] with regard to the relationship between settlements that were mostly tens of kms apart. Using a system that learnt the fuzzy membership function, the subjects were asked to specify the truth or falsehood of nearness for specific instances of pairs of settlements, one of which was the ground location.…”

Section: Modelling the Applicability Of Spatial Prepositionsmentioning

confidence: 99%

“…Knowledge of the applicability of different prepositions was acquired through a set of human subject experiments conducted in a lab and online, in which participants were asked to rate the suitability of a set of prepositions (based on the prior caption analysis) to particular configurations of the located object and a reference location (<toponym>) to which it is related by the preposition. These experiments were similar to those of for example Worboys [32] and Robinson [20] [21]. They differ though in that the subjects were told the context of the task was photo captioning, the scale of map data was adapted to the typical scale found in the caption analysis experiments and the subjects were asked to provide ratings of applicability of given prepositions using values on a Likert scale from 1 to 9.…”

Section: Introductionmentioning

confidence: 99%

Spatial Natural Language Generation for Location Description in Photo Captions

Hall

Jones

Smart

2015

Spatial Information Theory

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Abstract. We present a spatial natural language generation system to create captions that describe the geographical context of geo-referenced photos. An analysis of existing photo captions was used to design templates representing typical caption language patterns, while the results of human subject experiments were used to create field-based spatial models of the applicability of some commonly used spatial prepositions. The language templates are instantiated with geo-data retrieved from the vicinity of the photo locations. A human subject evaluation was used to validate and to improve the spatial language generation procedure, examples of the results of which are presented in the paper.

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“…The most common method of capturing human perception is as follows: To begin, subjects are presented with a small number (usually less than 100) of images, referred to as configurations, containing basic shapes. Subjects are then given a set of spatial relations (usually less than 10), and for each configuration, they are required to either answer a series of yes or no questions (i.e., whether a given relation describes the configuration (Robinson 1990)), or to rate a list of spatial relations based on how well they describe the configuration individually (Gapp 1995;Wang & Keller 1997, 1999Zhan 2002). The weaknesses in these methods are obvious.…”

Section: Introductionmentioning

confidence: 99%

A Study on how Humans Describe Relative Positions of Image Objects

Wang

Matsakis

Trick

et al. 2008

Headway in Spatial Data Handling

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Information describing the layout of objects in space is commonly conveyed through the use of linguistic terms denoting spatial relations that hold between the objects. Though progress has been made in the understanding and modelling of many individual relations, a better understanding of how human subjects use spatial relations together in natural language to is required. This paper outlines the design and completion of an experiment resulting in the collection of 1920 spoken descriptions from 32 human subjects; they describe the relative positions of a variety of objects within an image space. We investigate the spatial relations that the subjects express in their descriptions, and the terms through which they do so, in an effort to determine variations and commonalities. Analysis of the descriptions determines that common elements of spatial perception do indeed exist between subjects, and that the subjects are quite consistent with each other in the use of spatial relations.

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Interactive machine acquisition of a fuzzy spatial relation

Cited by 64 publications

References 8 publications

An Intelligent Spatial Proximity System Using Neurofuzzy Classifiers and Contextual Information

An Intelligent Spatial Proximity System Using Neurofuzzy Classifiers and Contextual Information

Spatial Natural Language Generation for Location Description in Photo Captions

A Study on how Humans Describe Relative Positions of Image Objects

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