A comparative study of texture measures with classification based on featured distributions

Ojala, Timo; Pietikäinen, Matti; Harwood, David

doi:10.1016/0031-3203(95)00067-4

Cited by 5,996 publications

(2,967 citation statements)

References 11 publications

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“…those used in vector quantisation or alphabet approaches, e.g. textons [54] or other local neighbourhood based features [1], [37], [40]. They exploit short-range, aperiodic (and periodic) spatial relationships.…”

Section: The Importance Of Three Types Of Data To Texture Perceptionmentioning

confidence: 99%

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Perceptually Motivated Image Features Using Contours

Dong

Chantler

2016

IEEE Trans. on Image Process.

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Abstract-Dong et al. examined the ability of 51 computational feature sets to estimate human perceptual texture similarity, however, none performed well for this task. While it is well-known that the human visual system is extremely adept at exploiting longer-range aperiodic (and periodic) "contour" characteristics in images, none of the investigated feature sets exploit higher order statistics (HOS) over larger image regions (>19×19 pixels). We therefore hypothesise that long-range HOS, in the form of contour data, are useful for perceptual texture similarity estimation.We present the results of a psychophysical experiment that shows that contour data are more important, than local image patches, or global 2nd-order data, to human observers for this task.Inspired by this finding, we propose a set of perceptually motivated image features (PMIF) that encode the long-range HOS computed from spatial and angular distributions of contour segments. We use two perceptual texture similarity estimation tasks to compare PMIF against the 51 feature sets referred to above and four commonly used contour representations. This new feature set is also examined in the context of two additional tasks: sketch-based image retrieval and natural scene recognition. The results show that the proposed feature set performs better, or at least comparably to, all the other feature sets. We attribute this promising performance to the fact that the proposed feature set exploits both short-range and long-range HOS.

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Section: The Importance Of Three Types Of Data To Texture Perceptionmentioning

confidence: 99%

“…In this experiment, the five best feature sets investigated in [19], namely, VZ-NBRHD [54], MRSAR [37], LBPBASIC [40], LBPHF [1] and RING & WEDGE [11], were utilised as baselines. The 7 measures obtained using the feature sets are shown in Fig.…”

Section: ) Retrieval Based Evaluation Experimentsmentioning

confidence: 99%

Perceptually Motivated Image Features Using Contours

Dong

Chantler

2016

IEEE Trans. on Image Process.

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“…The LBPs have shown to be efficient in texture classification [29] and face detection and recognition [27]. It also inspired many pedestrian detection studies as it is intuitive and easy-to-implement [28], [30].…”

Section: Related Workmentioning

confidence: 99%

A Novel Pedestrian Detector on Low-Resolution Images: Gradient LBP Using Patterns of Oriented Edges

Boudissa

Tan

Kim

et al. 2013

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper introduces a simple algorithm for pedestrian detection on low resolution images. The main objective is to create a successful means for real-time pedestrian detection. While the framework of the system consists of edge orientations combined with the local binary patterns (LBP) feature extractor, a novel way of selecting the threshold is introduced. Using the mean-variance of the background examples this threshold improves significantly the detection rate as well as the processing time. Furthermore, it makes the system robust to uniformly cluttered backgrounds, noise and light variations. The test data is the INRIA pedestrian dataset and for the classification, a support vector machine with a radial basis function (RBF) kernel is used. The system performs at state-of-the-art detection rates while being intuitive as well as very fast which leaves sufficient processing time for further operations such as tracking and danger estimation.

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“…LBP's also provide robust pattern related information. Ojala et al [6] carried out texture classification based on feature distributions of different texture measures and found that this method performed well when applied to Brodatz [10] textures. LBP is combined with the contrast of the texture which is the measure of local variations present in an image.…”

Section: Colour Texture Segmentationmentioning

confidence: 99%

“…Research on human visual system supports the processing of luminance and chrominance components separately. LBP and the DCT developed by Ojala et al [6] and Ng et al [7] respectively are used as the feature extraction techniques to extract features from the intensity plane, followed by the extraction of colour features from the chrominance planes. An unsupervised texture segmentation method [8] is used to segment the image.…”

Section: Introductionmentioning

confidence: 99%

Experimentation on the Use of Chromaticity Features, Local Binary Pattern, and Discrete Cosine Transform in Colour Texture Analysis

2003

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Abstract. This paper describes a method for colour texture analysis, which performs segmentation based on colour and texture information. The main goal of this approach is to examine the contribution of chromaticity features in the analysis of texture. Local binary pattern and discrete cosine transform are the techniques utilised as a tool to perform feature extraction. Segmentation is carried out based on an unsupervised texture segmentation method. The performance of the method is evaluated using different chromaticity features and also using the ROC curves. The results indicate that the inclusion of colour information improves the segmentation performance.

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A comparative study of texture measures with classification based on featured distributions

Cited by 5,996 publications

References 11 publications

Perceptually Motivated Image Features Using Contours

Perceptually Motivated Image Features Using Contours

A Novel Pedestrian Detector on Low-Resolution Images: Gradient LBP Using Patterns of Oriented Edges

Experimentation on the Use of Chromaticity Features, Local Binary Pattern, and Discrete Cosine Transform in Colour Texture Analysis

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