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Erschienen in:
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2011 | OriginalPaper | Buchkapitel

1. General Framework

verfasst von : Dr. Alejandro Héctor Toselli, Dr. Enrique Vidal, Prof. Francisco Casacuberta

Erschienen in: Multimodal Interactive Pattern Recognition and Applications

Verlag: Springer London

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Abstract

Lately, the paradigm for Pattern Recognition (PR) systems design is shifting from the concept of full-automation to systems where the decision process is conditioned by human feedback. This shift is motivated by the fact that full automation often proves elusive, or unnatural in many applications where technology is expected to assist rather than replace the human agents.
This chapter examines the challenges and research opportunities entailed by placing PR within the human-interaction framework; namely: (a) taking direct advantage of the feedback information provided by the user in each interaction step to improve raw performance; (b) acknowledging the inherent multimodality of interaction to improve overall system behavior and usability and (c) using the feedback-derived data to tune the system to the user behavior and the specific task considered, by means of adaptive learning techniques.
One of the most influential factors for the rapid development of PR technology in the last few decades is the nowadays commonly adopted assessment paradigm based on labeled training and testing corpora. This chapter includes a discussion about simple but realistic “user models” or interaction protocols and assessment criteria which allow the successful labeled corpus-based assessment paradigm to be applied also in the interactive scenario.
This chapter also provides an introduction to general approaches available to solve the underlying interactive search problems on the basis of existing methods to solve the corresponding non-interactive counterparts and an overview of modern machine learning approaches which can be useful in the interactive framework.

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Nächstes Kapitel Computer Assisted Transcription: General Framework
Fußnoten
1
True probabilities will be denoted as Pr (⋅), while modeled probabilities will be written as \(P_{\mathcal{M}}(\cdot)\), where the model \(\mathcal{M}\) can be omitted if it is understood from the context.
 
2
Moreover, we are completely ignoring here recent advances in karyotype analysis, such as fluorescent dye-based spectral karyotyping [45], which allow obtaining colored chromosome images and may significantly simplify the real problem of human karyotyping.
 
3
Note that each loss function has its own optimal Bayes’ decision rule, although the most widespread notation, \(\hat{h}\), does not explicitly highlight it.
 
4
This would be a first-order approach but, more generally, h′ can represent an adequate combination of the optimal hypotheses obtained in all previous interaction steps for the given x.
 
5
Although it might seem that under this approach the user is forced to interact after each hypothesis proposed by the IPR system; this is not the case since the set of possible feedbacks \(\mathcal{F}\) can be extended with a null feedback, ∅.
 
6
A less drastic assumption would keep the cross dependencies of f and x, leading to more interesting, intermediate fusion schemes [28].
 
7
A more general example is given in Chap. 2.
 
8
Note that we have been able to avoid the use of the unconditional probability Pr (x) in all the all the PR and IPR optimization (search) equations discussed in the previous sections.
 
9
Words for which the system has no previous example.
 
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Metadaten
Titel
General Framework
verfasst von
Dr. Alejandro Héctor Toselli
Dr. Enrique Vidal
Prof. Francisco Casacuberta
Copyright-Jahr
2011
Verlag
Springer London
Buch
Multimodal Interactive Pattern Recognition and Applications

Print ISBN: 978-0-85729-478-4

Electronic ISBN: 978-0-85729-479-1

Copyright-Jahr: 2011

DOI
https://doi.org/10.1007/978-0-85729-479-1_1

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