Copyright © 2014 by Intelligent Systems Laboratory, Computer Science Department, Technion - Israel Institute of Technology, Haifa 3200003, Israel. All rights reserved

A Segmentation Quality Measure Based On Rich Descriptors And Classification Methods

A Segmentation Quality Measure Based On Rich Descriptors And Classification MethodsBy: David Peles and Michael Lindenbaum

Most segmentation methods are based on a relatively simple score, designed to lend itself to relatively efficient optimization. We take the opposite approach and suggest more complex segmentation scores that are based on a mixture of on-line and off-line learning processes and rely on rich descriptors. The score is evaluated by a segmentation process which uses exploration-exploitation to search for good segments in various scales and shapes. We test our algorithm in a foreground-background segmentation task, given a minimal prior which is just a single seed point inside the object of interest. Results on two image databases are presented and compared with earlier approaches.

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Non-Local Characterization Of Scenery Images: Statistics, 3d Reasoning, And A Generative Model

Non-Local Characterization Of Scenery Images: Statistics, 3d Reasoning, And A Generative ModelBy: Tamar Avraham, Michael Lindenbaum

This work focuses on characterizing scenery images. We semantically divide the objects in natural landscape scenes into background and foreground and show that the shapes of the regions associated with these two types are statistically different. We then focus on the background regions. We study statistical properties such as size and shape, location and relative location, the characteristics of the boundary curves and the correlation of the properties to the region's semantic identity. Then we discuss the imaging process of a simplified 3D scene model and show how it explains the empirical observations. We further show that the observed properties suffice to characterize the gist of scenery images, propose a generative parametric graphical model, and use it to learn and generate semantic sketches of new images, which indeed look like those associated with natural scenery.

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Optimizing Gabor Filter Design For Texture Edge Detection And Classification

Optimizing Gabor Filter Design For Texture Edge Detection And ClassificationBy: Roman Sandler and Michael Lindenbaum

An effective and efficient texture analysis method, based on a new criterion for designing Gabor filter sets, is proposed. The commonly used filter sets are usually designed for optimal signal representation.We propose here an alternative criterion for designing the filter set. We consider a set of filters and its response to pairs of harmonic signals.
Two signals are considered separable if the corresponding two sets of vector responses are disjoint in at least one of the components. We propose an algorithm for deriving the set of Gabor filters that maximizes the fraction of separable harmonic signal pairs in a given frequency range. The resulting filters differ significantly from the traditional ones. We test these maximal harmonic discrimination (MHD) filters in several texture analysis tasks: clustering, recognition, and edge detection. It turns out that the proposed filters perform much better than the traditional ones in these tasks. They can achieve performance similar to that of state-of-the-art, distribution based (texton) methods, while being simpler and more computationally efficient.

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Unsupervised Estimation Of Segmentation Quality Using Nonnegative Factorization

Unsupervised Estimation Of Segmentation Quality Using Nonnegative FactorizationBy: Roman Sandler and Michael Lindenbaum

We propose an unsupervised method for evaluating image segmentation. Prevalent methods are typically based on valuating smoothness within segments and contrast between them. The proposed approach differs: it provides a meaningful, uantitative assessment of segmentation quality in precision/recall terms, applicable until now only for supervised evaluation. oreover, it builds on a new image model, which characterizes the segments as a mixture of basic feature distributions. The istributions are obtained by a nonnegative factorization (NMF) process and precision/recall estimates are then estimated from hem. As the estimates are based on the intrinsic properties of the image being evaluated and not on a comparison to typical mages (learning), they are relatively robust to context factors such as image quality or texture. Experimental results demonstrate he accuracy of the precision/recall estimates in comparison to human-judged ground truth. Finally, the unsupervised measure an be used to tune and improve the quality of popular segmentation algorithms.

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Nonnegative Matrix Factorization With Earth Mover’s Distance Metric

Nonnegative Matrix Factorization With Earth Mover’s Distance MetricBy: Roman Sandler and Michael Lindenbaum

Nonnegative Matrix Factorization (NMF) approximates a given data matrix as a product of two low rank nonnegative matrices, usually by minimizing the L2 or the KL distance between the data matrix and the matrix product. This factorization was shown to be useful for several important computer vision applications.
We propose here a new NMF algorithm that minimizes the Earth Mover’s Distance (EMD) error between the data and the matrix product. We propose an iterative NMF algorithm (EMD NMF) and prove its convergence. The algorithm is based on linear programming. We discuss the numerical difficulties of the EMD NMF and propose an efficient approximation.
Naturally, the matrices obtained with EMD NMF are different from those obtained with L2 NMF. We discuss these differences in the context of two challenging computer vision tasks – texture classification and face recognition – and demonstrate the advantages of the proposed method.

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