Multi-scale local pattern co-occurrence matrix for textural image classification

Textural image classification technologies have been extensively explored and widely applied in many areas. It is advantageous to combine both the occurrence and spatial distribution of local patterns to describe a texture. However, most existing state-of-the-art approaches for textural image classification only employ the occurrence histogram of local patterns to describe textures, without considering their co-occurrence information. And they are usually very time-consuming because of the vector quantization involved. Moreover, those feature extraction paradigms are implemented at a single scale. In this paper we propose a novel multi-scale local pattern co-occurrence matrix (MS_LPCM) descriptor to characterize textural images through four major steps. Firstly, Gaussian filtering pyramid preprocessing is employed to obtain multi-scale images; secondly, a local binary pattern (LBP) operator is applied on each textural image to create a LBP image; thirdly, the gray-level co-occurrence matrix (GLCM) is utilized to extract local pattern co-occurrence matrix (LPCM) from LBP images as the features; finally, all LPCM features from the same textural image at different scales are concatenated as the final feature vectors for classification. The experimental results on three benchmark databases in this study have shown a higher classification accuracy and lower computing cost as compared with other state-of-the-art algorithms.

Experimental comparison of approaches for feature extraction of facial attributes

In this paper, we compare the effectiveness of widely used approaches for representation of facial features in face images. Feature extraction is performed on face images for representation of four facial attributes, namely gender, age, race, and expression, by using discrete wavelet transform (DWT), Gabor wavelet, scale-invariant feature transform, local binary pattern (LBP), and Eigenfaces. After feature extraction and dimension reduction, demographic and expression classification is performed to identify the most discriminating techniques for representation of facial features. Extensive experiments are performed using publicly available face databases, namely Yale, Face95 Essex, and Cohn-Kanade (CK+) databases. Experimental results show that DWT, LBP, and Gabor wavelet methods are robust to variations of illumination, facial expression, and geometric transformations. Experimental results also show that race and expression are more difficult to predict than gender and age.

Intelligent facial emotion recognition using a layered encoding cascade optimization model

In this research, we propose a facial expression recognition system with a layered encoding cascade optimization model. Since generating an effective facial representation is a vital step to the success of facial emotion recognition, a modified Local Gabor Binary Pattern operator is first employed to derive a refined initial face representation and we then propose two evolutionary algorithms for feature optimization including (i) direct similarity and (ii) Pareto-based feature selection, under the layered cascade model. The direct similarity feature selection considers characteristics within the same emotion category that give the minimum within-class variation while the Pareto-based feature optimization focuses on features that best represent each expression category and at the same time provide the most distinctions to other expressions. Both a neural network and an ensemble classifier with weighted majority vote are implemented for the recognition of seven expressions based on the selected optimized features. The ensemble model also automatically updates itself with the most recent concepts in the data. Evaluated with the Cohn-Kanade database, our system achieves the best accuracies when the ensemble classifier is applied, and outperforms other research reported in the literature with 96.8% for direct similarity based optimization and 97.4% for the Pareto-based feature selection. Cross-database evaluation with frontal images from the MMI database has also been conducted to further prove system efficiency where it achieves 97.5% for Pareto-based approach and 90.7% for direct similarity-based feature selection and outperforms related research for MMI. When evaluated with 90° side-view images extracted from the videos of the MMI database, the system achieves superior performances with >80% accuracies for both optimization algorithms. Experiments with other weighting and meta-learning combination methods for the construction of ensembles are also explored with our proposed ensemble showing great adpativity to new test data stream for cross-database evaluation. In future work, we aim to incorporate other filtering techniques and evolutionary algorithms into the optimization models to further enhance the recognition performance.

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

A modified fuzzy min-max neural network with a genetic-algorithm-based rule extractor for pattern classification

In this paper, a two-stage pattern classification and rule extraction system is proposed. The first stage consists of a modified fuzzy min-max (FMM) neural-network-based pattern classifier, while the second stage consists of a genetic-algorithm (GA)-based rule extractor. Fuzzy if-then rules are extracted from the modified FMM classifier, and a ??don't care?? approach is adopted by the GA rule extractor to minimize the number of features in the extracted rules. Five benchmark problems and a real medical diagnosis task are used to empirically evaluate the effectiveness of the proposed FMM-GA system. The results are analyzed and compared with other published results. In addition, the bootstrap hypothesis analysis is conducted to quantify the results of the medical diagnosis task statistically. The outcomes reveal the efficacy of FMM-GA in extracting a set of compact and yet easily comprehensible rules while maintaining a high classification performance for tackling pattern classification tasks.

Capitanian (late Guadalupian, permian) global brachiopod palaeobigeography and latitudinal diversity pattern

A global database containing 3365 occurrences, 821 species and 251 genera of the Capitanian (Late Guadalupian, Permian) brachiopod faunas from 24 stations has been analyzed by cluster analysis using the Jaccard and Otsuka coefficients and the probabilistic index of similarity, nonmetric multidimensional scaling and minimum spanning tree. Two supergroups, three groups and six subgroups are revealed and interpreted as representing, respectively, two biotic realms (the Palaeoequatorial and Gondwanan Realms), two regions and six provinces. An additional realm (the Boreal Realm), based on the fauna from Spitsbergen, also appears recognizable although it also shows considerable similarities with southwestern North America and the northern margin of Gondwana as revealed by the statistical analysis. The Palaeoequatorial Realm can be further subdivided into the North America Region and the Asian Tethyan Region. The six biotic provinces are the Cathaysian Province in the Palaeotethys and Mesotethys, the Greenland-Svalbard Province in the Arctic region, the Austrazean Province in eastern Australia and New Zealand, the Grandian Province in western North America and the two transitional zones (the Himalayan Province in the southern temperate zone and the Sino–Mongolian–Japanese Province in the northern temperate zone). Polynomial regression analysis and rarefaction analysis indicate that the generic diversities of brachiopod faunas during the Capitanian peaked in the Palaeoequatorial Cathaysian Province and the two transitional zones (Himalayan Province and Sino–Mongolian–Japanese Province), but fell dramatically in the polar regions. The generic diversity of the Palaeoequatorial Grandian Province is apparently lower than in the two transitional zones of temperate palaeolatitudes, suggesting that the generic diversity of Capitanian brachiopod faunas does not exhibit a strict negative correlation with palaeolatitudes. This in turn would suggest that biogeographical determinants (such as geographical barriers, inhabitable area and ocean currents) other than latitude-related temperature control may also have played an important role in the dispersal of some brachiopods and the characterization of some local provinces and high diversities. The Capitanian global brachiopod palaeobiogeography is generally comparable with those in the Wuchiapingian and Changhsingian, but with some notable differences. These include: (1) that the Grandian Province of the Capitanian in western North America vanished after the end-Guadalupian regression, (2) that the western Tethyan Province of the Lopingian could not be distinguished in the Capitanian, and (3) that the Austrazean Province was larger in area than either in the Wuchiapingian or in the Changhsingian.

Methods for exploring implementation variation and local context within a cluster tandomised community intervention trial

Insignificant or modest findings in intervention trials may be attributable to poorly designed or theorised interventions, poorly implemented interventions, or inadequate evaluation methods. The pre-existing context may also account for the effects observed. A combination of qualitative and quantitative methods is outlined that will permit the determination of how context level factors might modify intervention effectiveness, within a cluster randomised community intervention trial to promote the health of mothers with new babies. The methods include written and oral narratives, key informant interviews, impact logs, and inter-organisational network analyses. Context level factors, which may affect intervention uptake, success, and sustainability are the density of inter-organisational ties within communities at the start of the intervention, the centrality of the primary care agencies expected to take a lead with the intervention, the extent of context-level adaptation of the intervention, and the amount of local resources contributed by the participating agencies. Investigation of how intervention effects are modified by context is a new methodological frontier in community intervention trial research.

Tenocyte responses to mechanical loading in vivo : A role for local insulin-like growth factor 1 signaling in early tendinosis in rats

Objective
To investigate tenocyte regulatory events during the development of overuse supraspinatus tendinosis in rats.

Methods
Supraspinatus tendinosis was induced by running rats downhill at 1 km/hour for 1 hour a day. Tendons were harvested at 4, 8, 12, and 16 weeks and processed for brightfield, polarized light, or transmission electron microscopy. The development of tendinosis was assessed semiquantitatively using a modified Bonar histopathologic scale. Apoptosis and proliferation were examined using antibodies against fragmented DNA or proliferating cell nuclear antigen, respectively. Insulin-like growth factor 1 (IGF-1) expression was determined by computer-assisted quantification of immunohistochemical reaction. Local IGF-1 signaling was probed using antibodies to phosphorylated insulin receptor substrate 1 (IRS-1) and ERK-1/2.

Results
Tendinosis was present after 12 weeks of downhill running and was characterized by tenocyte rounding and proliferation as well as by glycosaminoglycan accumulation and collagen fragmentation. The proliferation index was elevated in CD90+ tenocytes in association with tendinosis and correlated with increased local IGF-1 expression by tenocytes and phosphorylation of IRS-1 and ERK-1/2. Both apoptosis and cellular inflammation were absent at all time points.

Conclusion
In this animal model, early tendinosis was associated with local stimulation of tenocytes rather than with extrinsic inflammation or apoptosis. Our data suggest a role for IGF-1 in the load-induced tenocyte responses during the pathogenesis of overuse tendon disorders.

Simultaneous estimation of optical flow and object state: A modified approach to optical flow calculation

Differential optical flow methods are widely used within the computer vision community. They are classified as being either local, as in the Lucas-Kanade method, or global, as in the Horn-Schunck technique. As the physical dynamics of an object is inherently coupled into the behavior of its image in the video stream, in this paper, we use such dynamic parameter information in calculating optical flow when tracking a moving object using a video stream. Indeed, we use a modified error function in the minimization that contains physical parameter information. Further, the refined estimates of optical flow is used for better estimation of the physical parameters of the object in the simultaneous estimation of optical flow and object state(SEOS).

Local Austenite grain size distribution in hot bar rolling of AISI 4135 Steel

In this paper, the local distribution of austenite grain size (AGS) was experimentally determined by conducting single round-oval and square-diamond pass hot bar rolling experiments of AISI4135 steel. The rolling experiments were carried out using the laboratory mill. The local distribution of AGS was also determined numerically. In order to predict AGS distribution, the AGS evolution model was combined with three dimensional non-isothermal finite element analyses by adopting a modified additivity rule. AGS evolution model was experimentally determined from hot torsion test according to Hodgson's model. The predicted results were in a reasonably good agreement with experimental results.

The spatial pattern of natural selection when selection depends upon experience

Apostatic (frequency‐ or density‐dependent) selection, aposematic signals, and mate choice behavior generally require that the mean prey or potential mate density m value be high enough (above a threshold T) to result in sufficient encounter rates for the searcher to learn or retain the association between conspicuous signals and prey unprofitability, to forage apostatically, or to choose among mates. This assumes that all searchers experience , which implicitly assumes an even dispersion of targets among searcher territories. Uneven dispersion generates new phenomena. If , then only territories with local density x values that are greater than T favor experience‐based behavior, leading to spatially variable frequency‐ or density‐dependent selection intensity. As aggregation increases, the increase in percentage of targets in favorable territories ( ) is greater than the increase in the percentage of territories that are favorable. The relationship is reversed when . In both cases, because as few as 10% of the territories can contain 80% of the targets, only a few territory holders may account for most of the selection on most of the target population; accidents of experience in only a few searchers can have unexpectedly large effects on the target population. This also provides an explanation for high searcher behavior variation (personalities) : individuals from favorable territories will behave differently in behavioral experiments than those from unfavorable territories, at least with respect to similar kinds of targets. These effects will generate spatial heterogeneity in natural and sexual selection in what are otherwise uniform environments.

Knowledge discovery in multi-databases by analyzing local instances

Multi-databases mining is an urgent task. This thesis solves 4 key problems in multi-databases mining: Application-independent database classification - Local instance analysis model - Useful pattern discovery - Pattern synthesis.

Attacking anonymous web browsing at local area networks through browsing dynamics

The majority of current anonymous systems focus on improving anonymity at the network and website level in order to defend against traffic analysis attacks. However, the vulnerability of the connections between end users and the anonymous network do not attract any attention yet. For the first time, we reveal an end user browsing dynamics based attack on anonymous browsing systems at the LAN where the victim locates. This new attack method is fundamentally different from existing attack methodologies. In general, web surfers browse the web following certain patterns, such as requesting a web page, viewing it and requesting another page. The browsing pattern of a victim can be clearly observed by a local adversary when the victim is viewing the web without protection. Unfortunately, browsing dynamics releases rich information for attacking even though the web page content is encrypted. In order to show how a local eavesdropper can decipher which pages have been viewed with the knowledge of user browsing dynamics and the public information of a given website, we established a specific hidden Markov model to represent browsing dynamics for the website. By using this model, we can then identify the optimal of the accessed pages using the Viterbi algorithm. In order to confirm the effectiveness of the revealed attack method, we have conducted extensive experiments on a real data set. The results demonstrated that the attack accuracy can be more than 80%. A few possible counter-attack strategies are discussed at the end of the paper.

Local energy and pre-envelope

We observe that the local energy is the pre-envelope for analytic function. The maxima and phase of this function can be used to compute and classify visual features such as motion and stereo disparity, texture, etc. We examine the construction of new filters for computing Local Energy, and compare these filters with the Gabor filters and the three-point-filter of Venkatesh and Owens.