A study of Gaussian mixture models of color and texture features for image classification and segmentation

Permuter, Haim H.; Francos, J.M.; Jermyn, Ian H.

doi:10.1016/j.patcog.2005.10.028

Cited by 317 publications

(144 citation statements)

References 37 publications

Supporting

Mentioning

143

Contrasting

Unclassified

Order By: Relevance

“…Generally, the histogram can be precisely fitted by setting M to 5, and EM (Expectation-Maximum) algorithm was employed for iterative computation until parameters convergence (Permuter, 2006;Bilmes, 1998 …”

Section: ) Get Previous Knowledge Of Cloud-free Imagesmentioning

confidence: 99%

Automatic Mosaicking of Satellite Imagery Considering the Clouds

Kang

Pan

Chen

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:With the rapid development of high resolution remote sensing for earth observation technology, satellite imagery is widely used in the fields of resource investigation, environment protection, and agricultural research. Image mosaicking is an important part of satellite imagery production. However, the existence of clouds leads to lots of disadvantages for automatic image mosaicking, mainly in two aspects: 1) Image blurring may be caused during the process of image dodging, 2) Cloudy areas may be passed through by automatically generated seamlines. To address these problems, an automatic mosaicking method is proposed for cloudy satellite imagery in this paper. Firstly, modified Otsu thresholding and morphological processing are employed to extract cloudy areas and obtain the percentage of cloud cover. Then, cloud detection results are used to optimize the process of dodging and mosaicking. Thus, the mosaic image can be combined with more clear-sky areas instead of cloudy areas. Besides, clear-sky areas will be clear and distortionless. The Chinese GF-1 wide-field-of-view orthoimages are employed as experimental data. The performance of the proposed approach is evaluated in four aspects: the effect of cloud detection, the sharpness of clear-sky areas, the rationality of seamlines and efficiency. The evaluation results demonstrated that the mosaic image obtained by our method has fewer clouds, better internal color consistency and better visual clarity compared with that obtained by traditional method. The time consumed by the proposed method for 17 scenes of GF-1 orthoimages is within 4 hours on a desktop computer. The efficiency can meet the general production requirements for massive satellite imagery.

show abstract

Section: ) Get Previous Knowledge Of Cloud-free Imagesmentioning

confidence: 99%

Automatic Mosaicking of Satellite Imagery Considering the Clouds

Kang

Pan

Chen

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Therefore, for the image sequence, a pixel of the image is classified to belong to the foreground object if the probability of the pixel value is less than a predefined threshold. In [42], the GMMs are constructed over a variety of different color and texture feature spaces. Instead of using EM, Permuter et al apply the k-means clustering algorithm to reduce the high computation incurred by the EM algorithm.…”

Section: Gaussian Mixture Model (Gmm)mentioning

confidence: 99%

“…More complex methods to form the background model from a bunch of background images are proposed. The background model can be built by a single Gaussian or a mixture of Gaussians for each pixel [42,43], or by statistical parameters including the intensity change and chromaticity change for each pixel [44].…”

Section: Introductionmentioning

confidence: 99%

A Review on Video-Based Human Activity Recognition

et al. 2013

View full text Add to dashboard Cite

This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems. OPEN ACCESSComputers 2013, 2 89

show abstract

“…Texture information can be used for directly classify images or as additional band in the clustering process; in the last case, classification accuracies are generally improved (Wang et al, 2004;Puissant et al, 2005;Rao et al, 2002). Further, texture estimation is important because provides information about spatial and structural arrangement of objects, thanks to the strong correspondence between them and their pattern (Tso and Mather, 2001;Permuter et al, 2006). Even though colour is the most appealing feature, it is also the most vulnerable indexing parameter, since it strongly depends on image lighting conditions, pose and sensor characteristics.…”

Section: Introductionmentioning

confidence: 99%

Land Use Classification From VHR Aerial Images Using Invariant Colour Components and Texture

Movia¹,

Beinat²,

Sandri³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Very high resolution (VHR) aerial images can provide detailed analysis about landscape and environment; nowadays, thanks to the rapid growing airborne data acquisition technology an increasing number of high resolution datasets are freely available. In a VHR image the essential information is contained in the red-green-blue colour components (RGB) and in the texture, therefore a preliminary step in image analysis concerns the classification in order to detect pixels having similar characteristics and to group them in distinct classes. Common land use classification approaches use colour at a first stage, followed by texture analysis, particularly for the evaluation of landscape patterns. Unfortunately RGB-based classifications are significantly influenced by image setting, as contrast, saturation, and brightness, and by the presence of shadows in the scene. The classification methods analysed in this work aim to mitigate these effects. The procedures developed considered the use of invariant colour components, image resampling, and the evaluation of a RGB texture parameter for various increasing sizes of a structuring element. To identify the most efficient solution, the classification vectors obtained were then processed by a K-means unsupervised classifier using different metrics, and the results were compared with respect to corresponding user supervised classifications. The experiments performed and discussed in the paper let us evaluate the effective contribution of texture information, and compare the most suitable vector components and metrics for automatic classification of very high resolution RGB aerial images.

show abstract

A study of Gaussian mixture models of color and texture features for image classification and segmentation

Cited by 317 publications

References 37 publications

Automatic Mosaicking of Satellite Imagery Considering the Clouds

Automatic Mosaicking of Satellite Imagery Considering the Clouds

A Review on Video-Based Human Activity Recognition

Land Use Classification From VHR Aerial Images Using Invariant Colour Components and Texture

Contact Info

Product

Resources

About