Incremental spectral clustering and its application to topological mapping

This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is incremental – the spectral clustering algorithm is applied to the affinity matrix after each row/column is added – which makes it possible to inspect the clusters as new data points are added. The method is well suited to the problem of appearance-based, on-line topological mapping for mobile robots. In this problem domain, we show that we can reduce environment-dependent parameters of the clustering algorithm to just a single, intuitive parameter. Experimental results in large outdoor and indoor environments show that we can close loops correctly by computing only a fraction of the entries in the affinity matrix. The accompanying video clip shows how an example map is produced by the algorithm

Visual-hint Boundary to Segment Algorithm for Image Segmentation

Image segmentation has been a very active research topic in image analysis area. Currently, most of the image segmentation algorithms are designed based on the idea that images are partitioned into a set of regions preserving homogeneous intra-regions and inhomogeneous inter-regions. However, human visual intuition does not always follow this pattern. A new image segmentation method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is more consistent with human perceptions. VHBS abides by two visual hint rules based on human perceptions: (i) the global scale boundaries tend to be the real boundaries of the objects; (ii) two adjacent regions with quite different colors or textures tend to result in the real boundaries between them. It has been demonstrated by experiments that, compared with traditional image segmentation method, VHBS has better performance and also preserves higher computational efficiency.Comment: 45 page

Spectral clustering and fuzzy similarity measure for images segmentation

In image segmentation algorithms using spectral clustering, due to the size of the images, the computational load for the construction of the similarity matrix and the solution to the eigenvalue problem for the Laplacian matrix is high. Furthermore, the Gaussian kernel similarity measure is the most used, but it presents problems with irregular data distributions. This work proposes to perform a pre-segmentation or decimation by superpixels with the Simple Linear Iterative Clustering algorithm to reduce the computational cost, and to build the similarity matrix with a fuzzy measure based on the Fuzzy C-Means classifier, providing the algorithm a greater robustness against images with complex distributions and by spectral clustering the final segmentation is determined. Experimentally, it was found that the proposed approach obtains adequate segmentations, good clustering results and a comparable precision with respect to five algorithms; measuring performance under four determined validation metrics.En los algoritmos de segmentación de imágenes mediante agrupamiento espectral, debido al tamaño de las imágenes, la carga computacional para la construcción de la matriz de similitud y la solución al problema de valores propios para la matriz laplaciana son altos. Además, la medida de similitud más utilizada es el kernel gaussiano, el cual presenta problemas con distribuciones de datos irregulares. Este trabajo propone realizar una presegmentación o diezmado mediante superpíxeles con el algoritmo Simple Linear Iterative Clustering, para disminuir el costo computacional y construir la matriz de similaridad con una medida difusa basada en el clasificador Fuzzy C-Means, que proporciona al algoritmo una mayor robustez frente a imágenes con distribuciones complejas; mediante agrupamiento espectral se determina la segmentación final. Experimentalmente, se comprobó que el enfoque propuesto obtiene segmentaciones adecuadas, buenos resultados de agrupamiento y una precisión comparable respecto a cinco algoritmos, midiendo el desempeño bajo cuatro métricas de validación

Robust path-based spectral clustering with application to image segmentation

Spectral clustering and path-based clustering are two recently developed clustering approaches that have delivered impressive results in a number of challenging clus-tering tasks. However, they are not robust enough against noise and outliers in the data. In this paper, based on M-estimation from robust statistics, we develop a robust path-based spectral clustering method by defining a robust path-based simi-larity measure for spectral clustering under both unsupervised and semi-supervised settings. Our proposed method is significantly more robust than spectral clustering and path-based clustering. We have performed experiments based on both syn-thetic and real-world data, comparing our method with some other methods. In 1 particular, color images from the Berkeley Segmentation Dataset and Benchmark are used in the image segmentation experiments. Experimental results show that our method consistently outperforms other methods due to its higher robustness

Proceedings, MSVSCC 2016

Proceedings of the 10th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2016 at VMASC in Suffolk, Virginia