Hierarchical Temporal Memory (HTM) is still largely unknown by the pattern recognition community and only a few studies have been published in the scientific literature. This paper reviews HTM architecture and related learning algorithms by using formal notation and pseudocode description. Novel approaches are then proposed to encode coincidence-group membership (fuzzy grouping) and to derive temporal groups (maxstab temporal clustering). Systematic experiments on three line-drawing datasets have been carried out to better understand HTM peculiarities and to extensively compare it against other well-know pattern recognition approaches. Our results prove the effectiveness of the new algorithms introduced and that HTM, even if still in its infancy, compares favorably with other existing technologies.

/pdf/pattern-recognition-by-hierarchical-temporal-memory-4fifb8i4qr.pdf

Pattern Recognition by Hierarchical Temporal Memory

Most traditional pixel-based analyses are based on the digital number of each pixel. Whereas images can provide more details such as color, size, shape, and texture, object-oriented processing is more advantageous. Multiresolution segmentation, which was proposed by Baatz and Schape, is one of the most powerful segmentation algorithms. On the other hand, meaningful segmentation is the most important issue in object-oriented processing. Currently, meaningful segmentation, which is recommended by Baatz's multiresolution segmentation approach, is a trial-and-error task that is very tedious and time consuming. Therefore, a genetic algorithm (GA) is used for finding optimal parameters of Baatz's multiresolution segmentation approach for three building groups' meaningful segmentation. The optimal parameters are found by GA and its generality has been evaluated on a simulated image as well as some IKONOS and GeoEye image patches. The evaluations show the efficiency of GA for finding optimal multiresolution segmentation parameters for meaningful segmentation of the simulated image and the three groups of building images.

Optimization of multiresolution segmentation by using a genetic algorithm

The use of a general EM (expectation-maximization) algorithm in multi-spectral image classification is known to cause two problems: singularity of the variance-covariance matrix and sensitivity of randomly selected initial values. The former causes computation failure; the latter produces unstable classification results. This paper proposes a modified approach to resolve these defects. First, a modification is proposed to determine reliable parameters for the EM algorithm based on a k-means algorithm with initial centers obtained from the density function of the first principal component, which avoids the selection of initial centers at random. A second modification uses the principal component transformation of the image to obtain a set of uncorrelated data. The number of principal components as the input of the EM algorithm is determined by the principal contribution rate. In this way, the modification can not only remove singularity but also weaken noise. Experimental results obtained from two sets of remote sensing images acquired by two different sensors confirm the validity of the proposed approach.

/pdf/an-improved-em-algorithm-for-remote-sensing-classification-35o7ta6890.pdf

An improved EM algorithm for remote sensing classification

The growing availability of high-resolution satellite imagery provides an opportunity for identifying road objects. Most studies associated with road detection are scene-related and also based on the digital number of each pixel. Because images can provide more details (including color, size, shape, and texture), object-based processing is more advantageous. Therefore, in this paper, to handle the existing uncertainty of satellite image pixel values, using type-2 fuzzy set theory in combination with object-based image analysis is proposed. Because the main challenges of the type-2 fuzzy set are parameter tuning and extensive computations, a hybrid genetic algorithm (GA) consisting of Pittsburgh and cooperative-competitive learning schemes is proposed to address these problems. The most prominent feature of our research in this work is to establish a comprehensive object-based type-2 fuzzy logic system that enables us to detect roads in high-resolution satellite images with no training data. The validation assessment of road detection results using the proposed framework for independent images demonstrates the capability and efficiency of our method in identifying road objects. For more evaluation, a type-1 fuzzy logic system with the same structure as type-2 is tuned. Evaluations show that type-1 fuzzy logic system quality in training is very similar to that of the proposed type-2 fuzzy framework. However, in general, its lower accuracy, as inferred by validation assessments, makes the type-1 fuzzy logic system significantly different from the proposed type-2.

/pdf/designing-a-new-framework-using-type-2-fls-and-cooperative-fpjn1xwu3u.pdf

Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery

Object oriented analysis is widely used in interpretation of remote sensing images in comparison with pixel based approaches. A key step for achieving an acceptable classification result is meaningful image segmentation. Multi resolution segmentation is known as one of the most popular approaches in image segmentation that have been implemented in commercial software on the market, eCognition. However, this algorithm needs a set of optimum parameters which usually obtained by trial and error task. This technique not only is tedious and time consuming, also rely on the user's experience. So In this study in order to alleviate this problem, genetic algorithm is proposed to find the optimal parameters for multi resolution segmentation by focusing on road feature. This method is implemented on a pan-sharpened IKONOS image covering a part of Shiraz city, Iran. The results show that, with parameters found by GA, multi resolution segmentation accuracy is higher than obtained accuracy with parameters found by user. The evaluation of results confirms the importance of genetic algorithm to get optimal parameters.

/pdf/the-optimazation-of-multi-resolution-segmentation-of-243zsbc4hf.pdf

The optimazation of multi resolution segmentation of remotely sensed data using genetic alghorithm

A novel method that hybridizes genetic algorithm (GA) and expectation maximization (EM) algorithm for the classification of synthetic aperture radar (SAR) imagery is proposed by the finite Gaussian mixtures model (GMM) and multiscale autoregressive (MAR) model. This algorithm is capable of improving the global optimality and consistency of the classification performance. The experiments on the SAR images show that the proposed algorithm outperforms the standard EM method significantly in classification accuracy.

Multiscale modeling for classification of SAR imagery using hybrid EM algorithm and genetic algorithm

After Mountcastle proposed the theory that all parts of mammalian neocortex are uniform (1), more and more evidences were found to prove that no matter what function the field of neocortex provide, the organizing of cortex cells are same and have an hierarchical structure. It can be inferred every unit of neocortex process information using an identical algorithm. This means if people can find and imitate this algorithm, all intelligence work will be resolved in one way, the only difference is using different sensors. Base on this, Hawkins proposed a top- down model of neocortical operation (2), in which model, both continuous time and prediction play important roles in human's invariant recognition. Then George and Hawkins developed an inspired framework named "Hierarchical Temporal Memory" (3), using a hierarchical Bayesian network to perform prediction and temporal sequence in training the network, which partially implements Hawkins' model, and can provide a rather good performance in invariant recognition. We current study is extending their work by implementing the framework in visual category recognition field.

Object Recognition Using a Bayesian Network Imitating Human Neocortex

In this paper, a multiscale markov random field method for segmentation of the synthetic aperture radar (SAR) images is proposed. A classifier which inherits the strongpoint of the markov random field (MRF) and the multiscale autoregressive (MAR) model is designed. The MAR models are utilized to extract the multiscale feature of SAR image, which is used to train the MRF with the proposed algorithm, and then the SAR images is segmented by the trained random field. The experimental result demonstrates the effectiveness and efficiency of the proposed method.

Multiscale Markov Random Field Method for SAR Image Segmentation

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Multiscale modeling for classification of SAR imagery using hybrid EM algorithm and genetic algorithm

Object Recognition Using a Bayesian Network Imitating Human Neocortex

Multiscale Markov Random Field Method for SAR Image Segmentation