On deformable models for visual pattern recognition

Cheung, William K.; Yeung, Dit Yan; Chin, Roland T.

doi:10.1016/s0031-3203(01)00135-2

Cited by 29 publications

(8 citation statements)

References 128 publications

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“…The regularization parameter > 0 in (3) determines the relative sig-57 nificance of the penalty term in the objective function for the optimization problem. Note that the optimization criterion 59 in (3) is analogous to objective functions commonly used in energy minimization models such as deformable models 61 [20], with the penalty term P playing the role of an internal energy term. 63 The optimization problem formulated above can be solved in an iterative manner, resulting in an iterative metric adap-65 tation procedure [21].…”

Section: Metric Adaptation As An Optimization Problem 91mentioning

confidence: 99%

Locally linear metric adaptation with application to semi-supervised clustering and image retrieval

Chang

Yeung

2006

Pattern Recognition

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7Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised clustering, which performs 9 clustering in the presence of some background knowledge or supervisory information expressed as pairwise similarity or dissimilarity constraints. However, existing metric learning methods for semi-supervised clustering mostly perform global metric learning through a 11 linear transformation. In this paper, we propose a new metric learning method that performs nonlinear transformation globally but linear transformation locally. In particular, we formulate the learning problem as an optimization problem and present three methods for solving 13 it. Through some toy data sets, we show empirically that our locally linear metric adaptation (LLMA) method can handle some difficult cases that cannot be handled satisfactorily by previous methods. We also demonstrate the effectiveness of our method on some UCI data 15 sets. Besides applying LLMA to semi-supervised clustering, we have also used it to improve the performance of content-based image retrieval systems through metric learning. Experimental results based on two real-world image databases show that LLMA significantly 17 outperforms other methods in boosting the image retrieval performance.

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Section: Metric Adaptation As An Optimization Problem 91mentioning

confidence: 99%

Locally linear metric adaptation with application to semi-supervised clustering and image retrieval

Chang

Yeung

2006

Pattern Recognition

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“…They are often used to approximate the location and shape of object boundaries, on the basis of the reasonable assumption that boundaries are piecewise continuous or smooth [13].…”

Section: Second Step: Snake Active Contourmentioning

confidence: 99%

“…where I(x, y) is the image intensity, G s the Gaussian of standard deviation s, 5 the gradient operator, and c is a weight associated with image energies [13][14][15].…”

Section: Second Step: Snake Active Contourmentioning

confidence: 99%

Objective vocal fold vibration assessment from videokymographic images

Manfredi

Bocchi

Bianchi

et al. 2006

Biomedical Signal Processing and Control

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“…Deformable shape models [31][32][33][34] take a modelbased approach to object recognition by varying the shape of a model for representing an object while limiting the degree of model deformation. Our criterionbased optimization method for adjusting the locations of patterns in the input space resembles the energy minimization approach adopted by many deformable models.…”

Section: Related Previous Workmentioning

confidence: 99%