Metareasoning in the determination of image interpretation strategies

Bottoni, Paolo; Mussio, Piero; Protti, M.

doi:10.1016/0167-8655(94)90047-7

Cited by 14 publications

(6 citation statements)

References 13 publications

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“…This would allow decision combinations to be produced automatically or semi-automatically, rather than manually as we did in Section 6. Ultimately we would like to use decisionbased languages such as RSL for studying the problem of learning recognition strategies [13,14,15].…”

Section: Resultsmentioning

confidence: 99%

“…The decision-based approach provides intermediate interpretations and metrics for decision sequences (e.g. historical recall and precision) that could be used by machine learning algorithms to train or optimize individual decisions within a recognition strategy, or to learn recognition strategies themselves given a set of decisions and/or strategies [13,14,15].…”

Section: Introductionmentioning

confidence: 99%

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Decision-Based Specification and Comparison of Table Recognition Algorithms

Zanibbi

Blostein

Cordy

2008

Machine Learning in Document Analysis and Recognition

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Summary. Recognition algorithms are difficult to write and difficult to maintain. There is need for better tools to support the creation, debugging, optimization, and comparison of recognition algorithms. We propose an approach that centers on a process-oriented description. The approach is implemented using a new scripting language called RSL (Recognition Strategy Language), which captures the recognition decisions an algorithm makes as it executes. This semi-formal process-oriented description provides a powerful basis for developing and comparing recognition algorithms. Based on this description, we describe new metrics related to the sequence of decisions an algorithm makes during recognition. The capture of intermediate decision outputs and these new process-oriented metrics greatly extend the limited information available from final results and traditional results-oriented metrics such as recall and precision. Using a simple example, we illustrate how these new metrics can be used to understand and improve decisions within a recognition strategy. We believe these new metrics may also be applied in machine learning algorithms that construct optimal decision sequences from sets of decisions and/or strategies.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decision-Based Specification and Comparison of Table Recognition Algorithms

Zanibbi

Blostein

Cordy

2008

Machine Learning in Document Analysis and Recognition

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“…In the system IIMS, 10 implementing the deliberative approach quoted in the introduction, the state consists of the string d = features § pActions 0 plan where (a) features is a string of attributed symbols describing the results obtained so far and their relations;…”

Section: Preliminary Definitionsmentioning

confidence: 99%

“…However, rules are used to update the string features defined on W and metarules to update the string plan on C. Moreover, metarules access the features and pActions substrings to evaluate the situation: the use of two different alphabets avoids paradoxes and antinomies. 10 The format common to rules and metarules is the following: a name followed by one syntactic and one semantic parts.…”

Section: Understanding Strategies and Their Specificationmentioning

confidence: 99%

“…As to the first problem, a successful solution, recently applied to the automatic interpretation of technical drawings 9 and to semiautomatic liver cell counts, 10 adopts a deliberative approach, in which deliberation occurs on the state of the process. The state is evaluated by metareasoning mechanisms to determine the next actions to be performed, thus dynamically adapting the plan which prescribes the sequence of operations.…”

Section: Introductionmentioning

confidence: 99%

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Situated Image Understanding in a Multiagent Framework

Bianchi¹,

Bottoni²,

Mussio³

et al. 1998

Int. J. Patt. Recogn. Artif. Intell.

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The paper addresses the problem of controlling situated image understanding processes. Two complementary control styles are considered and applied cooperatively, a deliberative one and a reactive one. The role of deliberative control is to account for the unpredictability of situations, by dynamically determining which strategies to pursue, based on the results obtained so far and more generally on the state of the understanding process. The role of reactive control is to account for the variability of local properties of the image by tuning operations to subimages, each one being homogeneous with respect to a given operation. A variable organization of agents is studied to face this variability. The two control modes are integrated into a unified formalism describing segmentation and interpretation activities. A feedback from high level interpretation tasks to low level segmentation tasks thus becomes possible and is exploited to recover wrong segmentations. Preliminary results in the field of liver biopsy image understanding are shown to demonstrate the potential of the approach.

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Integration of neural networks and rule based systems in the interpretation of liver biopsy images

Bianchi

Diamantini

1995

Artificial Intelligence in Medicine

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Metareasoning in the determination of image interpretation strategies

Cited by 14 publications

References 13 publications

Decision-Based Specification and Comparison of Table Recognition Algorithms

Decision-Based Specification and Comparison of Table Recognition Algorithms

Situated Image Understanding in a Multiagent Framework

Integration of neural networks and rule based systems in the interpretation of liver biopsy images

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