13,098 research outputs found
A Fully Unsupervised Texture Segmentation Algorithm
This paper presents a fully unsupervised texture segmentation algorithm by using a modified discrete wavelet frames decomposition and a mean shift algorithm. By fully unsupervised, we mean the algorithm does not require any knowledge of the type of texture present nor the number of textures in the image to be segmented. The basic idea of the proposed method is to use the modified discrete wavelet frames to extract useful information from the image. Then, starting from the lowest level, the mean shift algorithm is used together with the fuzzy c-means clustering to divide the data into an appropriate number of clusters. The data clustering process is then refined at every level by taking into account the data at that particular level. The final crispy segmentation is obtained at the root level. This approach is applied to segment a variety of composite texture images into homogeneous texture areas and very good segmentation results are reported
Self-Paced Learning: an Implicit Regularization Perspective
Self-paced learning (SPL) mimics the cognitive mechanism of humans and
animals that gradually learns from easy to hard samples. One key issue in SPL
is to obtain better weighting strategy that is determined by minimizer
function. Existing methods usually pursue this by artificially designing the
explicit form of SPL regularizer. In this paper, we focus on the minimizer
function, and study a group of new regularizer, named self-paced implicit
regularizer that is deduced from robust loss function. Based on the convex
conjugacy theory, the minimizer function for self-paced implicit regularizer
can be directly learned from the latent loss function, while the analytic form
of the regularizer can be even known. A general framework (named SPL-IR) for
SPL is developed accordingly. We demonstrate that the learning procedure of
SPL-IR is associated with latent robust loss functions, thus can provide some
theoretical inspirations for its working mechanism. We further analyze the
relation between SPL-IR and half-quadratic optimization. Finally, we implement
SPL-IR to both supervised and unsupervised tasks, and experimental results
corroborate our ideas and demonstrate the correctness and effectiveness of
implicit regularizers.Comment: 12 pages, 3 figure
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