Use Of Co-occurrence Matrices Research Articles

A Quantitative Analysis of Co-occurrence Matrices in Ecological Systems: Measuring Connectance and Entropy

This study explores the use of co-occurrence matrices to quantify patterns of connectivity in ecological systems. By applying an entropy-based formula to both small-scale matrices and graphs, the analysis investigates how connections between species or system components can be modeled and understood through entropy and negentropy. The study also introduces a method for evaluating connectance using the ratio between observed and maximum negentropy values. Additionally, it compares this method to existing models, including Ricotta and Szeidl’s entropy measure and various graph-theory metrics. The findings demonstrate how these measures reflect system complexity and the interactions between components, offering insights into community structure and species coexistence. Parameters such as Whittaker’s Beta Diversity, evenness of eigenvalues, and nested similarity were examined to evaluate their correlations with connectance and entropy, in view to contribute to a deeper understanding the connections within ecological systems.

Foresight by scenarios - a literature review

The methods used in foresight studies are analysed under the approach of recent scenarios and applications, where new methods are evidenced. Sixty-six documents were analysed through a search equation in Scopus with the help of the software Vantage Point, taking into account criteria such as keywords, countries, methods used and applications; making use of co-occurrence matrices and cluster analysis. Nineteen new methods, techniques and tools were found, amongst which nano-tendencies, data mining, machine learning, genetic algorithms, and serious games stand out. In the same way, the stages for the construction of scenarios were identified and it was found that they do not fit into a single school or foresight approach.

Automatic plankton image recognition with co-occurrence matrices and Support Vector Machine

A long-standing problem in plankton ecology is sparseness of taxa-specific data. New optical imaging systems are becoming available which can acquire high-resolution data on the abun- dance and biomass of plankton taxa. The Video Plankton Recorder (VPR) has been designed and used for automatic sampling and visualization of major planktonic taxa at sea in real time, providing high-resolution data over a broad range of scales. Although these optical systems produce digital images of plankton that can be automatically identified by computer, the limited accuracy of auto- matic classification methods can reduce confidence in subsequent abundance estimates, especially in areas where a taxon is in low relative abundance. This paper describes an improved classification system for automatic identification of plankton taxa from digital images. Classifiers are trained from a set of images that were classified by human experts. The data set used to verify the classification system contains over 20 000 planktonic images manually sorted into 7 different categories. The new method uses co-occurrence matrices (COM) as the feature, and a Support Vector Machine (SVM) as the classifier. This new method is compared against a previous plankton recognition system, which used moment invariants, Fourier descriptors and granulometry as features and a learning vector quantization neural network as a classifier. The new method reduced the classification error rate from 39 to 28%. Subsequent plankton abundance estimates are improved by more than 50% in regions of low relative abundance. In general, the reduction in classification error was due to a combination of the use of COM and SVM.

Neural networks for the classification of image texture

In this paper, an extensive study of the efficiency of secondary features extracted from co-occurrence matrices and then direct use of co-occurrence matrices for the classification of texture images is carried out. First, the classification capabilities of individual, and then several combinations of, secondary features extracted from the co-occurrence matrices are examined. An adequate combination consisting of four of those features is hence established. A feed-forward artificial neural network is used as a classifier. The classification performance of the network as a function of the training data volume, its size and the training strategy are investigated. Next, an investigation is made into the direct use of co-occurrence matrix entries for classification. A simple technique, where the gray levels in the original images are grouped into a small number of equal bands, is employed in order to reduce the size of the matrix. It is shown that results obtained here compare favorably with those obtained earlier using the set of secondary features. The efficiency of the last technique is further demonstrated by means of considering a practical application. This involves the surface texture classification of wear particles found in the lubricating oil of moving machinery and engines. Again, it is shown that high recognition rates can be achieved when dealing with such classification tasks.

Use of co-occurrence matrices in the temporal domain

The concept of temporal co-occurrence matrices is introduced. The calculation and some of its features are described. Experimental results show that the temporal co-occurrence matrices can be applied to a wide range of interframe image processing, reducing the processing time involved.