Computational measures corresponding to perceptual textural features

Abbadeni, Noureddine; Dongdong, Zhou; Wang, S.

doi:10.1109/icip.2000.899601

Cited by 21 publications

(28 citation statements)

References 7 publications

(7 reference statements)

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“…Coarseness is introduced here to reflect the fact that primitives in a coarse texture are more clearly visible and distinguishable than those in a fine texture. So contrast is given by [4]:…”

Section: Definitionmentioning

confidence: 99%

“…Let Max(i, j) = 1 if pixel (i, j) is a maximum (either row maximum or column maximum) and Max(i, j) = 0 if pixel (i, j) is not a maxima. Coarseness C s can be written as follows [4]:…”

Section: Definitionmentioning

confidence: 99%

“…The degree of directionality N d is estimated as the number of points having the dominant orientation d and is given by [4] …”

Section: Definitionmentioning

confidence: 99%

“…Directionality estimation: Directionality involves the estimation of two parameters: orientation and degree of directionality. Orientation is estimated as the orientation of the gradient of the original image/autocovariance function and is given by [4] …”

Section: Definitionmentioning

confidence: 99%

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Multiple representations, similarity matching, and results fusion for content-based image retrieval

Abbadeni

2005

Multimedia Systems

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In this paper, we show how the use of multiple content representations and their fusion can improve the performance of content-based image retrieval systems. We consider the case of texture and propose a new algorithm for texture retrieval based on multiple representations and their results fusion. Texture content is modeled using two different models: the well-known autoregressive model and a perceptual model based on perceptual features such as coarseness and directionality. In the case of the perceptual model, two viewpoints are considered: perceptual features are computed based on the original images viewpoint and on the autocovariance function viewpoint (corresponding to original images). So we consider a total of three content representations. The similarity measure used is based on Gower's index of similarity. Simple results of the fusion models are used to merge search results returned by different representations. Experimentations and benchmarking carried out on the well-known Brodatz database show a drastic improvement in search effectiveness with the fused model without necessarily altering their efficiency in an important way.

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Section: Definitionmentioning

confidence: 99%

“…Let Max(i, j) = 1 if pixel (i, j) is a maximum (either row maximum or column maximum) and Max(i, j) = 0 if pixel (i, j) is not a maxima. Coarseness C s can be written as follows [4]:…”

Section: Definitionmentioning

confidence: 99%

“…The degree of directionality N d is estimated as the number of points having the dominant orientation d and is given by [4] …”

Section: Definitionmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

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Multiple representations, similarity matching, and results fusion for content-based image retrieval

Abbadeni

2005

Multimedia Systems

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“…To represent texture content, we use two different models, the autoregressive model and a perceptual model based on a set of perceptual features (8 is a causal simultaneous AR model with a non-symmetric half-plan (NSHP) neighborhood. The perceptual model is considered with two viewpoints: the original image's viewpoint and the autocovariance function (associated with original images) viewpoint.…”

Section: Xowlsoh 5hsuhvhqwdwlrqvmentioning

confidence: 99%

Invariant Image Retrieval: An Approach Based On Multiple Representations and Multiple Queries

Abbadeni¹

2010

Multimedia

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We present an approach based on multiple representations and multiple queries to tackle the problem of invariance in the framework of content-based image retrieval. We consider the case of textures. This approach, rather than to consider invariance at the representation level, considers it at the query level. We use two models to represent the visual content of textures, namely the autoregressive model and a perceptual model based on a set of perceptual features. The perceptual model is used with two viewpoints: the original image's viewpoint and the autocovariance function viewpoint. After a brief presentation and discussion of these multiple representation models/viewpoints, we present the invariant texture retrieval algorithm. This algorithm considers results fusion (merging) at two different levels: 1. The first level consists in merging results returned by different models/viewpoints (representations) for the same query in one results list using a linear results fusion model; 2. The second level consists in merging each fused list of different queries into a unique fused list using a round robin fusion scheme. Experimental retrieval results and benchmarking based on the precision/recall measures applied on a large image database show interesting results. ,QWURGXFWLRQContent-based image retrieval (CBIR) is a research area that has been active for more than a decade and many research results and prototypes have been carried out since then

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Perceptual Image Retrieval

Abbadeni

2006

Visual Information and Information Systems

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Computational measures corresponding to perceptual textural features

Cited by 21 publications

References 7 publications

Multiple representations, similarity matching, and results fusion for content-based image retrieval

Multiple representations, similarity matching, and results fusion for content-based image retrieval

Invariant Image Retrieval: An Approach Based On Multiple Representations and Multiple Queries

Perceptual Image Retrieval

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