KEYWORDS: Signal processing, Signal to noise ratio, Interference (communication), Error analysis, Signal analyzers, Optimization (mathematics), Signal attenuation, Monte Carlo methods, Data modeling, Associative arrays
Sparse approximation is critical to the applications of signal or image processing, and it is conducive to estimate the sparse signals with the joint efforts of transformation analysis. A simultaneous Bayesian framework is extended for sparse approximation by structured shared support, and a simultaneous sparse learning algorithm of structured approximation is proposed with transformation analysis, which leads to the feasible solutions more sensibly. Then the improvements of sparse Bayesian learning and iterative reweighting are embedded into the framework to achieve rapid convergence and high efficiency with robustness. Furthermore, the iterative optimization and transformation analysis are embedded in the overall learning process to obtain the relative optima for sparse approximation. Finally, compared with conventional reweighting algorithms for simultaneous sparse models with l1 and l2, simulation results present the preponderance of the proposed approach to solve the sparse structure and iterative redundancy in processing sparse signals. The fact indicates that the proposed method will be effective to sparsely approximate the various signals and images, which does accurately analyze the target in optimal transformation. It is envisaged that the proposed model could be suitable for a wide range of data in sparse separation and signal denoising.
KEYWORDS: Principal component analysis, Detection and tracking algorithms, Visualization, Image segmentation, Fourier transforms, Performance modeling, Data modeling, Image retrieval, Lithium, Control systems
A saliency detection method based on the PageRank algorithm and local spline regression (LSR) is proposed. Unlike the principles of most existing bottom-up methods, in the proposed method, saliency detection is considered a label propagation and regression problem. The input image is represented as two-scale graphs with homogeneous superpixels. Multiple color features and spatial information are effectively captured to define the relevance of each node to its surroundings. PageRank is used to assign the saliency value to each region depending on the similarity of the image elements with boundary cues. Furthermore, to comprehensively consider both foreground and background cues, a robust classifier based on LSR is employed to highlight more complete foreground regions. The integrated and refined pixel-level saliency map provides a significant performance boost in the final result. Extensive experiments on three large public datasets demonstrate that the proposed algorithm consistently achieves superior performance compared with state-of-the-art saliency models.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.