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

By Haim Henri Permuter, Joseph M. Francos, Ian H. Jermyn. Pattern Recognition, 2006

457 citations recorded by the source corpus.

The aims of this paper are two-fold: to define Gaussian mixture models (GMMs) of colored texture on several feature spaces and to compare the performance of these models in various classification tasks, both with each other and with other models popular in the literature. We construct GMMs over a variety of different color and texture feature spaces, with a view to the retrieval of textured color images from databases. We compare supervised classification results for different choices of color and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the 'color' and 'structure' information in order to improve the classification performances. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/38d1012b2aa847a3e7b5af0f0208e08665174b74

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Citations in the corpus

Earthquake Phase Association Using a Bayesian Gaussian Mixture Model (2021, methodology): “Because GaMMA is based on the GMM and does not perform a grid-search, its computational time scales only with the number of picks times the number of events, while the computational time of REAL scales with the number of picks times the number of grid points used.”
Sliced Wasserstein Distance for Learning Gaussian Mixture Models (2017, methodology): “GMMs have been utilized for: image representation [5, 16] to generate feature signatures, point set registration [23], adaptive contrast enhancement [9], inverse problems including super-resolution and deblurring [18, 54], time series classification [8], texture segmentation [42], and robotic visuom”
A Review on Video-Based Human Activity Recognition (2013, background): “In [42], the GMMs are constructed over a variety of different color and texture feature spaces.”
DivideMix: Learning with Noisy Labels as Semi-supervised Learning (2020, background): “In such cases, the GMM cannot effectively distinguish clean and noisy samples based on the loss distribution.”
CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation (2023, background): “Furthermore, several prior works [28, 29, 33, 43] have also claimed that the representation of categories in the feature space should be multiple activations, as shown in Fig.”
A probabilistic approach for damage identification and crack mode classification in reinforced concrete structures (2013, methodology): “, 2000; Reynolds and Rose, 1995), image processing (Permuter et al., 2006), dynamical systems and tracking (Povinelli et al.”
Unsupervised label noise modeling and loss correction (2019, methodology): “Modern CNNs trained with stochastic gra-0 Mixture models are a widely used unsupervised modeling technique (Stauffer & Grimson, 1999; Permuter et al., 2006; Ma & Leijon, 2011), with the Gaussian Mixture Model (GMM) (Permuter et al., 2006) being the most popular.”