Learning structured prediction models

Taskar, Ben; Chatalbashev, Vassil; Koller, Daphne; Guestrin, Carlos

doi:10.1145/1102351.1102464

Cited by 329 publications

(302 citation statements)

References 13 publications

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“…More precisely, it is multiclass classification algorithm fed by the set D E using a structured large-margin approach [12] which consists in minimizing the following criterion with respect to Q ∈ R S×A :…”

Section: Al and Irl Algorithmsmentioning

confidence: 99%

Learning from Demonstrations: Is It Worth Estimating a Reward Function?

Piot

Geist

Pietquin

2013

Advanced Information Systems Engineering

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Abstract. This paper provides a comparative study between Inverse Reinforcement Learning (IRL) and Apprenticeship Learning (AL). IRL and AL are two frameworks, using Markov Decision Processes (MDP), which are used for the imitation learning problem where an agent tries to learn from demonstrations of an expert. In the AL framework, the agent tries to learn the expert policy whereas in the IRL framework, the agent tries to learn a reward which can explain the behavior of the expert. This reward is then optimized to imitate the expert. One can wonder if it is worth estimating such a reward, or if estimating a policy is sufficient. This quite natural question has not really been addressed in the literature right now. We provide partial answers, both from a theoretical and empirical point of view.

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Section: Al and Irl Algorithmsmentioning

confidence: 99%

Learning from Demonstrations: Is It Worth Estimating a Reward Function?

Piot

Geist

Pietquin

2013

Advanced Information Systems Engineering

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“…We believe that this happens due to the limited number of training images per class to estimate the parameters of the SVM model. We plan to improve our method by modeling the dependencies between labels using, for example, structural learning methods [44,47]. This would prevent the following two issues observed in Fig.…”

Section: Resultsmentioning

confidence: 99%

Graph-based methods for the automatic annotation and retrieval of art prints

Carneiro

2011

Proceedings of the 1st ACM International Conference on Multimedia Retrieval

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The analysis of images taken from cultural heritage artifacts is an emerging area of research in the field of information retrieval. Current methodologies are focused on the analysis of digital images of paintings for the tasks of forgery detection and style recognition. In this paper, we introduce a graph-based method for the automatic annotation and retrieval of digital images of art prints. Such method can help art historians analyze printed art works using an annotated database of digital images of art prints. The main challenge lies in the fact that art prints generally have limited visual information. The results show that our approach produces better results in a weakly annotated database of art prints in terms of annotation and retrieval performance compared to state-of-the-art approaches based on bag of visual words.H.3.3Information storage and retrievalInformation search and retrieval|Retrieval models I.4.8Image Processing and Computer VisionScene Analysis|Object Recognition G.3Probability and StatisticsProbabilistic Algorithms General TermsAlgorithms, Measurement, Experimentation Keywords Content-based image retrieval and annotation, Art image annotation and retrieval, Graph-based Learning methods

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“…This is similar to the original form of the 1-D sequential CRFs of (Lafferty et al, 2001) with the difference that we use kernels to define this potential. Parallel to our work, researchers have proposed the use of kernels in CRF-type of models (Taskar et al, 2003) (Lafferty et al, 2004). Moreover, while designing graph potentials, recently other researchers have explored the use of different classifiers such as probit classifier (Qi et al, 2005) (Szummer and Qi, 2004) which will not yield a linear form of A i (.).…”

Section: Association Potentialmentioning

confidence: 93%

Discriminative Random Fields

Kumar

2014

Computer Vision

104

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Abstract.In this research we address the problem of classification and labeling of regions given a single static natural image. Natural images exhibit strong spatial dependencies, and modeling these dependencies in a principled manner is crucial to achieve good classification accuracy. In this work, we present Discriminative Random Fields (DRFs) to model spatial interactions in images in a discriminative framework based on the concept of Conditional Random Fields proposed by Lafferty et al (Lafferty et al., 2001). The DRFs classify image regions by incorporating neighborhood spatial interactions in the labels as well as the observed data. The DRF framework offers several advantages over the conventional Markov Random Field (MRF) framework. First, the DRFs allow to relax the strong assumption of conditional independence of the observed data generally used in the MRF framework for tractability. This assumption is too restrictive for a large number of applications in computer vision. Second, the DRFs derive their classification power by exploiting the probabilistic discriminative models instead of the generative models used for modeling observations in the MRF framework. Third, the interaction in labels in DRFs is based on the idea of pairwise discrimination of the observed data making it data-adaptive instead of being fixed a priori as in MRFs. Finally, all the parameters in the DRF model are estimated simultaneously from the training data unlike the MRF framework where the likelihood parameters are usually learned separately from the field parameters. We present preliminary experiments with man-made structure detection and binary image restoration tasks, and compare the DRF results with the MRF results.

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Learning structured prediction models

Cited by 329 publications

References 13 publications

Learning from Demonstrations: Is It Worth Estimating a Reward Function?

Learning from Demonstrations: Is It Worth Estimating a Reward Function?

Graph-based methods for the automatic annotation and retrieval of art prints

Discriminative Random Fields

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