Autocovariance-based perceptual textural features corresponding to human visual perception

Abbadeni, Noureddine; Ziou, Djemel; Wang, Shengrui

doi:10.1109/icpr.2000.903689

Cited by 35 publications

(32 citation statements)

References 6 publications

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“…Values of Spearman coefficient of rank-correlation r s found were as follows: for coarseness, r s = 0.913; for directionality, r s = 0.841; for contrast, r s = 0.755; and finally, for busyness, r s = 0.774. Compared to related works, our results were found more satisfactory (8), (9). These models and viewpoints were not constructed to be invariant to geometric and photometric transformations.…”

Section: Xowlsoh 5hsuhvhqwdwlrqvmentioning

confidence: 58%

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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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Section: Xowlsoh 5hsuhvhqwdwlrqvmentioning

confidence: 58%

“…The measure of contrast proposed is, obviously, sensitive to illumination conditions. For more details on the perceptual model, refer to (8), (9) and (6); for more details on the autoregressive model, refer to (10) and (5).…”

Section: Xowlsoh 5hsuhvhqwdwlrqvmentioning

confidence: 99%

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

Abbadeni¹

2010

Multimedia

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“…Different approaches for texture characterization have been developed over the years [3,4,5]. Haralick [3] proposed a classification of such approaches in two main groups: statistical methods (which analyze gray tone spatial distribution by computing some statistics on pixel intensities) and structural methods (which characterize texture by means of texels arranged in a certain way that is given by a set of rules).…”

Section: Introductionmentioning

confidence: 99%

Texture Measuring by Means of Perceptually-Based Fineness Functions

Chamorro-Martı́nez

Martínez-Jiménez

2009

Pattern Recognition and Image Analysis

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Abstract. Although texture is a widely analyzed property in computer vision, models which relate this feature with human perception are needed. In this paper, a model that associates computational fineness measures with the human perception of this type of texture property is proposed. The fineness perception is collected from human subjects, performing an aggregation of their assessments. Considering a wide variety of measures, a coarseness-fineness function relating these features with the human assesments is obtained as the function which provides the best fit of the collected data.

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“…Other authors consider texture as a set of basic items called texels (or texture primitives), arranged in a certain way [1,2]. Moreover, it is usual for humans to describe visual textures according to some "textural concepts" like coarseness, orientation, regularity [3]. To describe such concepts, linguistic labels are used (e.g.…”

Section: Introductionmentioning

confidence: 99%

Modelling Coarseness in Texture Images by Means of Fuzzy Sets

Chamorro-Martı́nez

Galan-Perales

Sánchez

et al. 2006

Lecture Notes in Computer Science

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Abstract.In this paper we model the concept of "coarseness", typically used in texture image descriptions, by means of fuzzy sets. Specifically, we relate representative measures of this kind of texture with its presence degree. To obtain these "presence degrees", we collect assessments from polls filled by human subjects, performing an aggregation of these assessments by means of OWA operators. Using this collected data, and some statistics as reference set, the membership function corresponding to the fuzzy set "coarseness" is modelled.

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Autocovariance-based perceptual textural features corresponding to human visual perception

Cited by 35 publications

References 6 publications

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

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

Texture Measuring by Means of Perceptually-Based Fineness Functions

Modelling Coarseness in Texture Images by Means of Fuzzy Sets

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