Similarity and features of natural textures.

Heaps, Christopher; Handel, Stephen

doi:10.1037/0096-1523.25.2.299

Cited by 82 publications

(62 citation statements)

References 46 publications

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“…MDS representations have their origins in, and some considerable status as, plausible models of human conceptual structure, particularly in relation to lowlevel, continuous sensory stimulus domains (Shepard, 1957(Shepard, , 1987(Shepard, , 1994. For this reason, MDS representations have been used as a tool for analyzing the psychological similarity structure of a variety of stimulus domains (e.g., Glushko, 1975;Jones, Roberts, & Holman, 1978;Heaps & Handel, 1999). In addition, they are used as the representational basis of a number of successful cognitive models, including the Generalized Context Model (Nosofsky, 1984(Nosofsky, , 1986, and ALCOVE (Kruschke, 1992).…”

Section: Introductionmentioning

confidence: 99%

Avoiding the dangers of averaging across subjects when using multidimensional scaling

Lee

Pope

2003

Journal of Mathematical Psychology

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Ashby, Maddox and Lee (Psychological Science, 5 (3) 144) argue that it can be inappropriate to fit multidimensional scaling (MDS) models to similarity or dissimilarity data that have been averaged across subjects. They demonstrate that the averaging process tends to make dissimilarity data more amenable to metric representations, and conduct a simulation study showing that noisy data generated using one distance metric, when averaged, may be better fit using a different distance metric. This paper argues that a Bayesian measure of MDS models has the potential to address these difficulties, because it takes into account data-fit, the number of dimensions used by an MDS representation, and the precision of the data. A method of analysis based on the Bayesian measure is demonstrated through two simulation studies with accompanying theoretical analysis. In the first study, it is shown that the Bayesian analysis rejects those MDS models showing better fit to averaged data using the incorrect distance metric, while accepting those that use the correct metric. In the second study, different groups of simulated 'subjects' are assumed to use different underlying configurations. In this case, the Bayesian analysis rejects MDS representations where a significant proportion of subjects use different configurations, or when their dissimilarity judgments contain significant amounts of noise. It is concluded that the Bayesian analysis provides a simple and principled means for systematically accepting and rejecting MDS models derived from averaged data. r

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Section: Introductionmentioning

confidence: 99%

Avoiding the dangers of averaging across subjects when using multidimensional scaling

Lee

Pope

2003

Journal of Mathematical Psychology

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“…They concluded that the axes correlated with the visual properties of repetitiveness, orientation and complexity. These results were questioned by Heaps & Hande [8] who argued that a dimensional model of texture is inappropriate and that texture similarity judgements are context dependent. Better results have been obtained for computational similarity by Long & Leow [9] and Petrou et al [11] however, these again used the limited Brodatz album which provides a relatively sparse sampling of texture space.…”

Section: Introductionmentioning

confidence: 59%

Perceptual Similarity: A Texture Challenge

Clarke¹,

Halley²,

Newell³

et al. 2011

Procedings of the British Machine Vision Conference 2011

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Over the last thirty years evaluation of texture analysis algorithms has been dominated by two databases: Brodatz has typically been used to provide single images of approximately 100 texture classes, while CUReT consists of multiple images of 61 physical samples captured under a variety of illumination conditions. While many highly successful approaches have been developed for classification, the challenging question of measuring perceived inter-class texture similarity has rarely been addressed. In this paper we introduce a new texture database which includes a collection of 334 samples together with perceptual similarity data collected from experiments with 30 human observers. We have tested four of the leading texture algorithms and they provide accurate (≈ 100%) performance in a CUReT style classification task, however, a second experiment shows that resulting inter-class distances do not correlate well with the perceptual data.

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“…The structure of the space of all textures, however, is extremely complex as shown by research in human vision, e.g., [Richards and Koenderink 1995;Heaps and Handel 1999]. Quoting Heaps and Handel [1999], "there is no fixed set of dimensions that characterize natural textures."…”

Section: Introductionmentioning

confidence: 98%

Texture design using a simplicial complex of morphable textures

Matusik

Zwicker

Durand

2005

ACM SIGGRAPH 2005 Papers

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We present a system for designing novel textures in the space of textures induced by an input database. We capture the structure of the induced space by a simplicial complex where vertices of the simplices represent input textures. A user can generate new textures by interpolating within individual simplices. We propose a morphable interpolation for textures, which also defines a metric used to build the simplicial complex. To guarantee sharpness in interpolated textures, we enforce histograms of high-frequency content using a novel method for histogram interpolation. We allow users to continuously navigate in the simplicial complex and design new textures using a simple and efficient user interface. We demonstrate the usefulness of our system by integrating it with a 3D texture painting application, where the user interactively designs desired textures.

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Similarity and features of natural textures.

Cited by 82 publications

References 46 publications

Avoiding the dangers of averaging across subjects when using multidimensional scaling

Avoiding the dangers of averaging across subjects when using multidimensional scaling

Perceptual Similarity: A Texture Challenge

Texture design using a simplicial complex of morphable textures

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