Abstraction in Artificial Intelligence and Complex Systems

Saitta, Lorenza; Zucker, Jean-Daniel

doi:10.1007/978-1-4614-7052-6

Cited by 47 publications

(36 citation statements)

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“…Cartwright (1989: 187) and McMullin (1985: 258) both argue that abstraction only occurs in cases where irrelevant facts about the system under study are omitted. We can see links here as well to one of the meanings of abstraction identified by Saitta and Zucker (2013), through the link to the retention of relevant information, and by extension, the omission of irrelevant information. Jones (op.…”

Section: The Different Aspects Of Abstractionmentioning

confidence: 80%

On the notion of abstraction in systemic functional linguistics

Williams

Russell

Irwin

2017

Functional Linguist.

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This article provides an in-depth analysis of the theoretical dimensions of stratification, instantiation, and delicacy within Systemic Functional Linguistics (SFL). It does so by examining the nature of the concept of 'abstraction' with respect to these terms. The claim is that, although the same term is used in each case, the precise meaning of abstraction differs depending on the theoretical focus. This conclusion is reached by examining three related, but distinct, factors: (1) omission of detail, (2) generalisation, and (3) decontextualisation. By using these criteria, this article develops a typology of abstraction types for SFL. The conclusion reached is that, although the dimensions of delicacy and instantiation can be described using the criteria above, the explicit ordering behind the dimension of stratification cannot. The paper concludes by examining a proposal that attempts to account for the explicit ordering between strata in terms of supervenience. We argue that this attempt fails to accurate describe inter-stratal relations in the theory. Therefore, the basis of this ordering is in need of further investigation.

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Section: The Different Aspects Of Abstractionmentioning

confidence: 80%

On the notion of abstraction in systemic functional linguistics

Williams

Russell

Irwin

2017

Functional Linguist.

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“…To construct a more compact KB, hierarchical categories are abstracted to form a higher level of semantics (Lorenza Saitta, 2013) (e.g., closet and shelf belong to the wardrobe). All categorical attributes of indoor scenes are given in Fig.…”

Section: Evidence Of the Knowledge Basementioning

confidence: 99%

A hierarchical inferential method for indoor scene classification

Jiang

Liu

et al. 2017

International Journal of Applied Mathematics and Computer Science

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Indoor scene classification forms a basis for scene interaction for service robots. The task is challenging because the layout and decoration of a scene vary considerably. Previous studies on knowledge-based methods commonly ignore the importance of visual attributes when constructing the knowledge base. These shortcomings restrict the performance of classification. The structure of a semantic hierarchy was proposed to describe similarities of different parts of scenes in a fine-grained way. Besides the commonly used semantic features, visual attributes were also introduced to construct the knowledge base. Inspired by the processes of human cognition and the characteristics of indoor scenes, we proposed an inferential framework based on the Markov logic network. The framework is evaluated on a popular indoor scene dataset, and the experimental results demonstrate its effectiveness.

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“…Roughly speaking, the model can be considered as an improvement over the original HoG by calculating and combining object templates of different scales. Because it is natural and human-like to represent semantic concepts abstractly, 38 it has become increasingly popular to organize and express semantics in a hierarchical manner. Thus, Felzenszwalb proposed the method for building cascade classifiers for the DPM model to significantly improve its detection speed.…”

Section: Object Detection and Classificationmentioning

confidence: 99%

Hierarchy-associated semantic-rule inference framework for classifying indoor scenes

Dai

Liu

et al. 2016

J. Electron. Imaging

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Typically, the initial task of classifying indoor scenes is challenging, because the spatial layout and decoration of a scene can vary considerably. Recent efforts at classifying object relationships commonly depend on the results of scene annotation and predefined rules, making classification inflexible. Furthermore, annotation results are easily affected by external factors. Inspired by human cognition, a scene-classification framework was proposed using the empirically based annotation (EBA) and a match-over rule-based (MRB) inference system. The semantic hierarchy of images is exploited by EBA to construct rules empirically for MRB classification. The problem of scene classification is divided into low-level annotation and high-level inference from a macro perspective. Low-level annotation involves detecting the semantic hierarchy and annotating the scene with a deformable-parts model and a bag-of-visual-words model. In high-level inference, hierarchical rules are extracted to train the decision tree for classification. The categories of testing samples are generated from the parts to the whole. Compared with traditional classification strategies, the proposed semantic hierarchy and corresponding rules reduce the effect of a variable background and improve the classification performance. The proposed framework was evaluated on a popular indoor scene dataset, and the experimental results demonstrate its effectiveness. Yu et al.: Hierarchy-associated semantic-rule inference framework for classifying indoor scenes Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/22/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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Abstraction in Artificial Intelligence and Complex Systems

Cited by 47 publications

References 0 publications

On the notion of abstraction in systemic functional linguistics

On the notion of abstraction in systemic functional linguistics

A hierarchical inferential method for indoor scene classification

Hierarchy-associated semantic-rule inference framework for classifying indoor scenes

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