Graphical Image Classification Combining an Evolutionary Algorithm and Binary Particle Swarm Optimization

Biomedical journal articles contain a variety of image types that can be broadly classified into two categories: regular images, and graphical images. Graphical images can be further classified into four classes: diagrams, statistical figures, flow charts, and tables. Automatic figure type identification is an important step toward improved multimodal (text + image) information retrieval and clinical decision support applications. This paper describes a feature-based learning approach to automatically identify these four graphical figure types. We apply Evolutionary Algorithm (EA), Binary Particle Swarm Optimization (BPSO) and a hybrid of EA and BPSO (EABPSO) methods to select an optimal subset of extracted image features that are then classified using a Support Vector Machine (SVM) classifier. Evaluation performed on 1038 figure images extracted from ten BioMedCentral® journals with the features selected by EABPSO yielded classification accuracy as high as 87.5%

Data Visualization Classification Using Simple Convolutional Neural Network Model

Data visualization is developed from the need to display a vast quantity of information more transparently. Data visualization often incorporates important information that is not listed anywhere in the document and enables the reader to discover significant data and save it in longer-term memory. On the other hand, Internet search engines have difficulty processing data visualization and connecting visualization and the request submitted by the user. With the use of data visualization, all blind individuals and individuals with impaired vision are left out. This article utilizes machine learning to classify data visualizations into 10 classes. Tested model is trained four times on the dataset which is preprocessed through four stages. Achieved accuracy of 89 % is comparable to other methods’ results. It is showed that image processing can impact results, i.e. increasing or decreasing level of details in image impacts on average classification accuracy significantly

GIS-based production of digital soil map for Nigeria

Soil, a valuable natural resource can be said to play a part across the range of human existence and its knowledge is fundamental to its utilization and management. Soil maps provide a means of gaining understanding about the soil, but limitations in accuracy, revision and mode of presentation– relating to graphics or digits–of such maps seem to stimulate lack of soil data in many places. However, much of the human activity has been treated with significant uncertainties, and many environmental processes relating to soil are poorly understood. Digital soil mapping seems to hold a strong prospect for addressing these challenges, with an interesting flexibility for utilizing many recent technologies. The present study proposes a digital soil map for Nigeria, and other thematicmaps based on soil properties, using geographic information system (GIS) technology. Existing graphical soil maps are the primary data sources. Although, the study is specific to making improved soil data available for the study area, the results tend to support the assumption that significantprospects are held by digital soil mapping in enhancing the knowledge of soil and its provision for a range of human activities.Key words: Soil, Soil maps, Digital soil map, GIS, Soil Database, Soil thematic maps, Query

Scientific chart image recognition and interpretation

Ph.DDOCTOR OF PHILOSOPH

Chart recognition and interpretation in document images

Ph.DDOCTOR OF PHILOSOPH

Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification

Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v