Recently, research in the field of content-based image retrieval has attracted a lot of attention. Nevertheless, most existing methods cannot be easily applied to medical image databases, as global image descriptions based on color, texture, or shape do not supply sufficient semantics for medical applications. The concept for content-based image retrieval in medical applications (IRMA) is therefore based on the separation of the following processing steps: categorization of the entire image; registration with respect to prototypes; extraction and query-dependent selection of local features; hierarchical blob representation including object identification; and finally, image retrieval. Within the first step of processing, images are classified according to image modality, body orientation, anatomic region, and biological system. The statistical classifier for the anatomic region is based on Gaussian kernel densities within a probabilistic framework for multiobject recognition. Special emphasis is placed on invariance, employing a probabilistic model of variability based on tangent distance and an image distortion model. The performance of the classifier is evaluated using a set of 1617 radiographs from daily routine, where the error rate of 8.0% in this six-class problem is an excellent result, taking into account the difficulty of the task. The computed posterior probabilities are furthermore used in the subsequent steps of the retrieval process.

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The principle of maximum entropy is a powerful framework that can be used to estimate class posterior probabilities for pattern recognition tasks. In this paper, we show how this principle is related to the discriminative training of Gaussian mixture densities using the maximum mutual information criterion. This leads to a relaxation of the constraints on the covariance matrices to be positive (semi-)definite.

Statistical classification using tangent vectors and classification based on local features are two successful methods for various image recognition problems. These two approaches tolerate global and local transformations of the images, respectively. Tangent vectors can be used to obtain global invariance with respect to small affine transformations and line thickness, for example. On the other hand, a classifier based on local representations admits the distortion of parts of the image.

. In this paper, we present a combined classication approach

In statistical pattern recognition, we use probabilistic models within the task of assigning observations to one of a set of predefined classes, like e.g. images of handwritten digits to one of the classes `0' to `9'. The principle of maximum entropy is a powerful framework that can be used to estimate class posterior probabilities for pattern recognition tasks. It is a conceptually simple and easily extensible model that allows to estimate a large number of free parameters reliably. We show how to apply this framework to object recognition and compare the results to other state-of-the-art approaches in experiments with the well known US Postal Service handwritten digits recognition task. We also introduce a simple but effective heuristic method for speeding up the algorithms used to determine the model parameters.

Abstract. In this paper, we present a kernel trick embedded Gaussian Mixture Model (GMM), called kernel GMM. The basic idea is to embed kernel trick into EM algorithm and deduce a parameter estimation algorithm for GMM in feature space. Kernel GMM could be viewed as a Bayesian Kernel Method. Compared with most classical kernel methods, the proposed method can solve problems in probabilistic framework. Moreover, it can tackle nonlinear problems better than the traditional GMM. To avoid great computational cost problem existing in most kernel methods upon large scale data set, we also employ a Monte Carlo sampling technique to speed up kernel GMM so that it is more practical and efficient. Experimental results on synthetic and real-world data set demonstrate that the proposed approach has satisfing performance. 1

GI subjects: image understanding (1.0.4), machine learning (1.1.3) In this paper, we present a statistical framework for the recognition of multiple objects in an image, which is a generalization of the Bayesian decision rule. The approach takes into account the interdependence of several operations including recognition and transformation of the objects and segmentation. We present first experimental results on single objects and on artificially generated scenes. Although the computational complexity of the approach is high, the additional effort seems justified and there is potential for reduction of complexity. 1

We integrate the tangent method into a statistical framework for classification analytically and practically. The resulting consistent framework for adaptation allows us to efficiently estimate the tangent vectors representing the variability. The framework improves classification results on two real-world pattern recognition tasks from the domains handwritten character recognition and automatic speech recognition.