Multiresolution image classification by hierarchical modeling with two-dimensional hidden Markov models

Li, Jia; Gray, Robert M.; Olshen, Richard A.

doi:10.1109/18.857794

Cited by 88 publications

(3 citation statements)

References 32 publications

(42 reference statements)

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“…In order to estimate the MAP p(c j i |d j ), we need to employ Bayesian approach for capturing dependencies between dyadic squares at different scales. Though many approximation techniques [26], [27], [28], [29] are derived for a practical computation of MAP, the Hidden Markov Tree (HMT) by Choi [30] is proven to be a feasible solution. Choi [30] introduces hidden label tree modelling instead of joint probability estimation in high-dimensional data of dyadic squares.…”

Section: Multi-scale Maximum a Posteriormentioning

confidence: 99%

Multi-scale discriminant saliency with wavelet-based Hidden Markov Tree modelling

Ngo

Ang

Seng

et al. 2014

Computers & Electrical Engineering

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Supposed saliency is a binary classification between centre and surround classes, saliency value is measured as their discriminant power. As the features are defined by sizes of chosen windows, a saliency value at each location is varied accordingly. This paper proposes computing saliency as discriminant power in multiple dyadic scales of Wavelet Hidden Markov Tree (HMT), in which two consecutive dyadic scales provide surrounding and central features, organized in a quad-tree structure. Their discriminant power is estimated as maximum a posterior probability (MAP) by Expectation-Maximization (EM) iterations. Then, a final saliency value is the maximum discriminant power generated among these scales. Standard quantitative tools and qualitative assessments are used for evaluating the proposed multi-scale discriminant saliency (MDIS) against the well-know information based approach AIM on its image collection with eye-tracking data. Simulation results are presented and analysed to verify the validity of MDIS as well as point out its limitation for further research direction

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Section: Multi-scale Maximum a Posteriormentioning

confidence: 99%

Multi-scale discriminant saliency with wavelet-based Hidden Markov Tree modelling

Ngo

Ang

Seng

et al. 2014

Computers & Electrical Engineering

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“…In order to capture the spatial dependence of gray-level values that contribute to the perception of texture, a two-dimensional dependence texture analysis matrix is discussed for texture consideration. In the literature, different kinds of textural features have been proposed, such as multichannel filtering features, fractal-based features, and co-occurrence features (Haralick, 1979;Li, Gray, & Olshen, 2000;Zhang, Gong, Low, & Smoliar, 1995). For our classification purposes, the co-occurrence features are selected as the basic texture feature detectors due to their good performance in many pattern recognition applications, including medical image processing, remote sensing, and content-based retrieval.…”

Section: Information Sysetm (Rule Stored)mentioning

confidence: 99%

Content-Based Image Classification and Retrieval

Ali

2007

International Journal of Intelligent Information Technologies

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Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. Thus, it is necessary to develop appropriate information systems to efficiently manage these datasets. Image classification and retrieval is one of the most important services that must be supported by such systems. The most common approach used is content-based image retrieval (CBIR) systems. This paper presents a new application of rough sets to feature reduction, classification, and retrieval for image databases in the framework of content-based image retrieval systems. The suggested approach combines image texture features with color features to form a powerful discriminating feature vector for each image. Texture features are extracted, represented, and normalized in an attribute vector, followed by a generation of rough set dependency rules from the real value attribute vector. The rough set reduction technique is applied to find all reducts with the minimal subset of attributes associated with a class label for classification.

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“…Waveletdomain HMM well represent the intrinsic properties of the wavelet coefficients and provide a powerful and tractable model for signals. Therefore, it was quickly adopted by a wide range of applications, including de-noising [27], [28], detection, classification [29], segmentation [30], texturing [31], and compressive sensing [32], [33]. However, when using the expectation-maximization algorithm in reference [19], a potential drawback to the HMM framework is the computationally expensive iterative training.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Statistical Characteristics of Remote Sensing Big Data in the Wavelet Transform Domain

Wang

Zhong

Ranjan

et al. 2014

IEEE Trans. Emerg. Topics Comput.

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Since it is difficult to deal with big data using traditional models and algorithms, predicting and estimating the characteristics of big data is very important. Remote sensing big data consist of many largescale images that are extremely complex in terms of their structural, spectral, and textual features. Based on multiresolution analysis theory, most of the natural images are sparse and have obvious clustering and persistence characters when they are transformed into another domain by a group of basic special functions. In this paper, we use a wavelet transform to represent remote sensing big data that are large scale in the space domain, correlated in the spectral domain, and continuous in the time domain. We decompose the big data set into approximate multiscale detail coefficients based on a wavelet transform. In order to determine whether the density function of wavelet coefficients in a big data set are peaky at zero and have a heavy tailed shape, a two-component Gaussian mixture model (GMM) is employed. For the first time, we use the expectation-maximization likelihood method to estimate the model parameters for the remote sensing big data set in the wavelet domain. The variance of the GMM with changing of bands, time, and scale are comprehensively analyzed. The statistical characteristics of different textures are also compared. We find that the cluster characteristics of the wavelet coefficients are still obvious in the remote sensing big data set for different bands and different scales. However, it is not always precise when we model the long-term sequence data set using the GMM. We also found that the scale features of different textures for the big data set are obviously reflected in the probability density function and GMM parameters of the wavelet coefficients.INDEX TERMS Big data computing, remote sensing image processing, wavelet, parameters estimation.

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Multiresolution image classification by hierarchical modeling with two-dimensional hidden Markov models

Cited by 88 publications

References 32 publications

Multi-scale discriminant saliency with wavelet-based Hidden Markov Tree modelling

Multi-scale discriminant saliency with wavelet-based Hidden Markov Tree modelling

Content-Based Image Classification and Retrieval

Estimating the Statistical Characteristics of Remote Sensing Big Data in the Wavelet Transform Domain

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