Jan Puzicha

Shape matching and object recognition using shape contexts

This paper presents my work on computing shape models that are computationally fast and invariant basic transformations like translation, scaling and rotation. In this paper, I propose shape detection using a feature called shape context. Shape context describes all boundary points of a shape with respect to any single boundary point. Thus it is descriptive of the shape of the object. Object recognition can be achieved by matching this feature with a priori knowledge of the shape context of the boundary points of the object. Experimental results are promising on handwritten digits, trademark images.

Empirical evaluation of dissimilarity measures for color and texture

This paper empirically compares nine image dissimilarity measures that are based on distributions of color and texture features summarizing over 1,000 CPU hours of computational experiments. Ground truth is collected via a novel random sampling scheme for color and via an image partitioning method for texture. Quantitative performance evaluations are given for classification, image retrieval, and segmentation tasks, and for a wide variety of dissimilarity measures. It is demonstrated how the selection of a measure, based on large scale evaluation, substantially improves the quality of classification, retrieval, and unsupervised segmentation of color and texture images.

Empirical Evaluation of Dissimilarity Measures for Color and Texture

This paper empirically compares nine families of image dissimilarity measures that are based on distributions of color and texture features summarizing over 1000 CPU hours of computational experiments. Ground truth is collected via a novel random sampling scheme for color, and by an image partitioning method for texture. Quantitative performance evaluations are given for classification, image retrieval, and segmentation tasks, and for a wide variety of dissimilarity measure parameters. It is demonstrated how the selection of a measure, based on large scale evaluation, substantially improves the quality of classification, retrieval, and unsupervised segmentation of color and texture images.

Non-parametric similarity measures for unsupervised texture segmentation and image retrieval

In this paper we propose and examine non-parametric statistical tests to define similarity and homogeneity measures for textures. The statistical tests are applied to the coefficients of images filtered by a multi-scale Gabor filter bank. We demonstrate that these similarity measures are useful for both, texture based image retrieval and for unsupervised texture segmentation, and hence offer a unified approach to these closely related tasks. We present results on Brodatz-like micro-textures and a collection of real-word images.

Unsupervised texture segmentation in a deterministic annealing framework

We present a novel optimization framework for unsupervised texture segmentation that relies on statistical tests as a measure of homogeneity. Texture segmentation is formulated as a data clustering problem based on sparse proximity data. Dissimilarities of pairs of textured regions are computed from a multiscale Gabor filter image representation. We discuss and compare a class of clustering objective functions which is systematically derived from invariance principles. As a general optimization framework, we propose deterministic annealing based on a mean-field approximation. The canonical way to derive clustering algorithms within this framework as well as an efficient implementation of mean-field annealing and the closely related Gibbs sampler are presented. We apply both annealing variants to Brodatz-like microtexture mixtures and real-word images.

A theory of proximity based clustering: structure detection by optimization

Abstract In this paper, a systematic optimization approach for clustering proximity or similarity data is developed. Starting from fundamental invariance and robustness properties, a set of axioms is proposed and discussed to distinguish different cluster compactness and separation criteria. The approach covers the case of sparse proximity matrices, and is extended to nested partitionings for hierarchical data clustering. To solve the associated optimization problems, a rigorous mathematical framework for deterministic annealing and mean-field approximation is presented. Efficient optimization heuristics are derived in a canonical way, which also clarifies the relation to stochastic optimization by Gibbs sampling. Similarity-based clustering techniques have a broad range of possible applications in computer vision, pattern recognition, and data analysis. As a major practical application we present a novel approach to the problem of unsupervised texture segmentation, which relies on statistical tests as a measure of homogeneity. The quality of the algorithms is empirically evaluated on a large collection of Brodatz-like micro-texture Mondrians and on a set of real–word images. To demonstrate the broad usefulness of the theory of proximity based clustering the performances of different criteria and algorithms are compared on an information retrieval task for a document database. The superiority of optimization algorithms for clustering is supported by extensive experiments.

Histogram clustering for unsupervised image segmentation

This paper introduces a novel statistical mixture model for probabilistic grouping of distributional (histogram) data. Adopting the Bayesian framework, we propose to perform annealed maximum a posteriori estimation to compute optimal clustering solutions. In order to accelerate the optimization process, an efficient multiscale formulation is developed. We present a prototypical application of this method for the unsupervised segmentation of textured images based on local distributions of Gabor coefficients. Benchmark results indicate superior performance compared to K-means clustering and proximity-based algorithms.

Support vector machines for land usage classification in Landsat TM imagery

Land usage classification is an essential part of many remote sensing applications for mapping, inventory, and yield estimation. In this contribution, we evaluate the potential of the support vector machines for remote sensing applications. Moreover, we expand this discriminative technique by a novel Bayesian approach to estimate the confidence of each classification. These estimates are combined with a priori knowledge about topological relations of class labels using a contextual classification step based on the iterative conditional mode algorithm (ICM). As shown for Landsat TM imagery, this strategy is highly competitive and outperforms several commonly used classification schemes.

On spatial quantization of color images

Image quantization and digital halftoning, two fundamental image processing problems, are generally performed sequentially and, in most cases, independent of each other. Color reduction with a pixel-wise defined distortion measure and the halftoning process with its local averaging neighborhood typically optimize different quality criteria or, frequently, follow a heuristic approach without reference to any quantitative quality measure. In this paper, we propose a new model to simultaneously quantize and halftone color images. The method is based on a rigorous cost-function approach which optimizes a quality criterion derived from a simplified model of human perception. It incorporates spatial and contextual information into the quantization and thus overcomes the artificial separation of quantization and halftoning. Optimization is performed by an efficient multiscale procedure which substantially alleviates the computational burden. The quality criterion and the optimization algorithms are evaluated on a representative set of artificial and real-world images showing a significant image quality improvement compared to standard color reduction approaches. Applying the developed cost function, we also suggest a new distortion measure for evaluating the overall quality of color reduction schemes.

Unsupervised segmentation of textured images by pairwise data clustering

A novel approach to unsupervised texture segmentation is presented which is formulated as a combinatorial optimization problem known as pairwise data clustering with a sparse neighborhood structure. Pairwise dissimilarities between texture blocks are measured in terms of distribution differences of multi-resolution features. The feature vectors are based on a Gabor wavelet image representation. To efficiently solve the data clustering problem a deterministic annealing algorithm on the basis of a mean field approximation is derived. An application to collages of Brodatz-like microtexture is demonstrated. The adequacy of the proposed segmentation cost function is statistically validated. The deterministic annealing algorithm outperforms its stochastic variants in terms of quality and efficiency.