Analysis of similarity measurements in CBIR using clustered tamura features for biomedical images

Content based image retrieval (CBIR) is an important research topic in many applications, in particular in the biomedical field. In this domain, the CBIR has the aim of helping to improve the diagnosis, retrieving images of patients for which a diagnosis has already been made, similar to the current image. The main issue of CBIR is the selection of the visual contents (feature descriptors) of the images to be extracted for a correct image retrieval. The second issue is the choice of the similarity measurement to use to compare the feature descriptors of the query image to ones of the other images of the database. This paper focuses on a comparison among different similarity measurements in CBIR, with particular interest to a biomedical images database. The adopted technique for CBIR is based on clustered Tamura features. The selected similarity measurements are used both to evaluate the adopted technique for CBIR and to estimate the stability of the results. A comparison with some methods in literature has been carried out, showing the best results for the proposed technique

A SVM-based cursive character recognizer

Abstract This paper presents a cursive character recognizer, a crucial module in any cursive word recognition system based on a segmentation and recognition approach. The character classification is achieved by using support vector machines (SVMs) and a neural gas. The neural gas is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not. Once this is done for every letter, the character recognition is performed by SVMs. A database of 57 293 characters was used to train and test the cursive character recognizer. SVMs compare notably better, in terms of recognition rates, with popular neural classifiers, such as learning vector quantization and multi-layer-perceptron. SVM recognition rate is among the highest presented in the literature for cursive character recognition

Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm

In this paper the problem of estimating the intrinsic dimension of a data set is investigated. An approach based on the Grassberger-Procaccia's algorithm has been studied. Since this algorithm does not yield accurate measures in high-dimensional data sets, an empirical procedure has been developed. Grassberger-Procaccia's algorithm was tested on two different benchmarks and was compared to a TRN-based method

Assessing the effects of Bt maize on the non-target pest Rhopalosiphum maidis by demographic and life-history measurement endpoints.

AbstractThe most commercialized Bt maize plants in Europe were transformed with genes which express a truncated form of the insecticidal delta-endotoxin (Cry1Ab) from the soil bacterium Bacillus thuringiensis (Bt) specifically against Lepidoptera. Studies on the effect of transgenic maize on non-target arthropods have mainly converged on beneficial insects. However, considering the worldwide extensive cultivation of Bt maize, an increased availability of information on their possible impact on non-target pests is also required. In this study, the impact of Bt-maize on the non-target corn leaf aphid, Rhopalosiphum maidis, was examined by comparing biological traits and demographic parameters of two generations of aphids reared on transgenic maize with those on untransformed near-isogenic plants. Furthermore, free and bound phenolics content on transgenic and near-isogenic plants were measured. Here we show an increased performance of the second generation of R. maidis on Bt-maize that could be attributable to indirect effects, such as the reduction of defense against pests due to unintended changes in plant characteristics caused by the insertion of the transgene. Indeed, the comparison of Bt-maize with its corresponding near-isogenic line strongly suggests that the transformation could have induced adverse effects on the biosynthesis and accumulation of free phenolic compounds. In conclusion, even though there is adequate evidence that aphids performed better on Bt-maize than on non-Bt plants, aphid economic damage has not been reported in commercial Bt corn fields in comparison to non-Bt corn fields. Nevertheless, Bt-maize plants can be more easily exploited by R. maidis, possibly due to a lower level of secondary metabolites present in their leaves. The recognition of this mechanism increases our knowledge concerning how insect-resistant genetically modified plants impact on non-target arthropods communities, including tritrophic web interactions, and can help support a sustainable use of genetically modified crops

Modeling Green Peach Aphid populations exposed to elicitors inducing plant resistance on peach

Matrix Population Models (MPMs) are not commonly used to simulate arthropod population dynamics with applications to pest control assessment in agricultural context. However, an increasing body of studies are prompting the finding of optimization techniques to reduce uncertainty in matrix parameters estimation. Indeed, uncertainty in parameters estimates may lead to significant management implications. Here we present a case study where MPMs are used for assessing the efficacy of treatment with elicitors inducing plant resistance against pathogen, such as laminarin, for the control of the Green Peach Aphid (Myzus persicae Sulzer) populations on peach. Such demographic approach could be particularly suitable to study this kind of compounds, which are mainly characterized by causing sub-lethal effects rather than acute mortality. An artificially assembled system [1] was arranged since it is well suited to follow the fate and behavior of a population exposed to elicitors activating chemical defense in plant. The obtained data, consisting of population time series, were used to generate a stage-classified projection matrix. The general model used to simulate population dynamics consists of a matrix containing i) survival probabilities (the probability of growing and moving to the next stage and the probability of surviving and remaining in the same stage), and ii) fecundities of the population. Most of the used methods for estimating the parameter values of stage-classified models rely on following cohorts of identified individuals [2]. However, in this study the observed data consisted of a time-series of population vectors where individuals are not distinguished. The relationship between the observed data and the values of the matrix parameters that produced the series involves an estimation process called inverse problem. Since all demographic analyses rely on how much the estimated parameters of the matrix are able to represent population dynamics, a Genetic algorithm for inverse parameter estimation was used in order to find a better model fit for the observed stage class distributions. These results were compared to those obtained by the quadratic programming method [3] used for determining the set of parameters that minimizes the residual between the collected data and the model output. REFERENCES: 1. Macfadyen, S., Banks, J.E., Stark, J.D., Davies, A.P., 2014. Using semifield studies to examine the effects of pesticides on mobile terrestrial invertebrates. Annu. Rev. Entomol. 59, 383-404. 2. Caswell, H., 2001. Matrix population models, second ed. Sinauer Associates Inc., Massachusetts. 3. Wood, S.N., 1994. Obtaining birth and mortality patterns from structured population trajectories. Ecol. Monogr. 64, 23-44

A survey of kernel and spectral methods for clustering

Clustering algorithms are a useful tool to explore data structures and have been employed in many disciplines. The focus of this paper is the partitioning clustering problem with a special interest in two recent approaches: kernel and spectral methods. The aim of this paper is to present a survey of kernel and spectral clustering methods, two approaches able to produce nonlinear separating hypersurfaces between clusters. The presented kernel clustering methods are the kernel version of many classical clustering algorithms, e.g., K-means, SOM and neural gas. Spectral clustering arise from concepts in spectral graph theory and the clustering problem is configured as a graph cut problem where an appropriate objective function has to be optimized. An explicit proof of the fact that these two paradigms have the same objective is reported since it has been proven that these two seemingly different approaches have the same mathematical foundation. Besides, fuzzy kernel clustering methods are presented as extensions of kernel K-means clustering algorithm. (C) 2007 Pattem Recognition Society. Published by Elsevier Ltd. All rights reserved