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Feature Analysis has become a very critical task in data analysis and visualization. Graph structures are very flexible in terms of representation and may encode important information on features but are challenging in regards to layout being adequate for analysis tasks. In this study, we propose and develop similarity-based graph layouts with the purpose of locating relevant patterns in sets of features, thus supporting feature analysis and selection. We apply a tree layout in the first step of the strategy, to accomplish node placement and overview based on feature similarity. By drawing the remainder of the graph edges on demand, further grouping and relationships among features are revealed. We evaluate those groups and relationships in terms of their effectiveness in exploring feature sets for data analysis. Correlation of features with a target categorical attribute and feature ranking are added to support the task. Multidimensional projections are employed to plot the dataset based on selected attributes to reveal the effectiveness of the feature set. Our results have shown that the tree-graph layout framework allows for a number of observations that are very important in user-centric feature selection, and not easy to observe by any other available tool. They provide a way of finding relevant and irrelevant features, spurious sets of noisy features, groups of similar features, and opposite features, all of which are essential tasks in different scenarios of data analysis. Case studies in application areas centered on documents, images and sound data demonstrate the ability of the framework to quickly reach a satisfactory compact representation from a larger feature set.

This article introduces an automatic approach for the segmentation of coloured natural scene images based on graphs and the propagation of labels originally designed for communities detection in complex networks. Images are initially pre‐segmented with super‐pixels, followed by feature extraction using colour information of each super‐pixels. The resulting graph consists of vertices which represent super‐pixels, whereas the edge weights are a measure of similarity between super‐pixels. The resulting segmentation corresponds to the propagation of labels among the vertices. In this article, three strategies for propagating labels have been formulated: (i) iterative propagation (ILP), (ii) recursive propagation (RLP) and (iii) a weighted recursive propagation (WRLP). The experiments have shown that the proposed methods, when compared to other state‐of‐the‐art methods, produce better results in terms of segmentation quality and processing time.

Image segmentation is an important task in image processing, usually employed in more complex computer vision tasks. In graph clustering-based segmentation approaches, the image is modeled by a graph, in which vertices are generally represented by pixels and edges by weights that denote similarity between pixels. The problems associated with graph-based approaches usually concern the computational cost and the high cardinality of the graphs, which translates into the large number of vertices and edges necessary to generate an adequate representation of the image. Among segmentation approaches with graphs, those based on detection of communities in complex networks, such as Label Propagation, in particular because they have a lower computational cost, stand out. However, such methods when applied directly to images, do not generate accurate results, in addition to being non-deterministic, which is an undesirable quality in image segmentation. On the other hand, superpixels techniques, which combine several pixels, are important not only in reducing the cardinality of the graphs, but also in providing greater descriptive power compared to a single pixel. This doctoral thesis presents a new family of segmentation methods for images of large natural scenes images based on the Label Propagation and superpixels method, with deterministic behavior and which uses specific information from the image domain. Algorithms were developed for both automatic segmentation (SGLP-Simple Graph Label Propagation and MGLP-Multi-level Label Propagation), and for interactive segmentation (IGLP-Interactive Graph Label Propagation), which demand user assistance. Quantitative results show a PRI precision of 0.83 and error percentage of Er 6.13%, for the automatic and interactive version, respectively. Results were also obtained in the processing time of 0.0048 s and 0.29 s, for automatic and interactive segmentation. These results were corroborated in several experiments on standard data sets. When compared with related methods, the results of the methods are superior both in mean precision and time for automatic segmentation. As for the interactive segmentation method (IGLP), segmentation mean precision was slightly outperformed by state of the art methods, but run in shorter times.