Tensor error correction for corrupted values in visual data

ABSTRACT The multi-channel image or the video clip has the natural form of tensor. The values of the tensor can be corrupted due to noise in the acquisition process. We consider the problem of recovering a tensor L of visual data from its corrupted observations X = L + S, where the corrupted entries S are unknown and unbounded, but are assumed to be sparse. Our work is built on the recent studies about the recovery of corrupted low-rank matrix via trace norm minimization. We extend the matrix case to the tensor case by the definition of tensor trace norm i

Spatiotemporal Saliency Detection: State of Art

Saliency detection has become a very prominent subject for research in recent time. Many techniques has been defined for the saliency detection.In this paper number of techniques has been explained that include the saliency detection from the year 2000 to 2015, almost every technique has been included.all the methods are explained briefly including their advantages and disadvantages. Comparison between various techniques has been done. With the help of table which includes authors name,paper name,year,techniques,algorithms and challenges. A comparison between levels of acceptance rates and accuracy levels are made

From Pixels to Region: A Salient Region Detection Algorithm for Location-Quantification Image

Image saliency detection has become increasingly important with the development of intelligent identification and machine vision technology. This process is essential for many image processing algorithms such as image retrieval, image segmentation, image recognition, and adaptive image compression. We propose a salient region detection algorithm for full-resolution images. This algorithm analyzes the randomness and correlation of image pixels and pixel-to-region saliency computation mechanism. The algorithm first obtains points with more saliency probability by using the improved smallest univalue segment assimilating nucleus operator. It then reconstructs the entire saliency region detection by taking these points as reference and combining them with image spatial color distribution, as well as regional and global contrasts. The results for subjective and objective image saliency detection show that the proposed algorithm exhibits outstanding performance in terms of technology indices such as precision and recall rates

Salient object detection employing robust sparse representation and local consistency

Many sparse representation (SR) based salient object detection methods have been presented in the past few years. Given a background dictionary, these methods usually detect the saliency by measuring the reconstruction errors, leading to the failure for those images with complex structures. In this paper, we propose to replace the traditional SR model with a robust sparse representation (RSR) model, for salient object detection, which replaces the least squared errors by the sparse errors. Such a change dramatically improves the robustness of the saliency detection in the existence of non-Gaussian noise, which is the case in most practical applications. By virtual of RSR, salient objects can equivalently be viewed as the sparse but strong “outliers” within an image so that the salient object detection problem can be reformulated to a sparsity pursuit one. Moreover, we jointly utilize the representation coefficients and the reconstruction errors to construct the saliency measure in the proposed method. Finally, we integrate a local consistency prior among spatially adjacent regions into the RSR model in order to uniformly highlight the whole salient object. Experimental results demonstrate that the proposed method significantly outperforms the traditional SR based methods and is competitive with some current state-of-the-art methods, especially for those images with complex structures

Structured Learning with Parsimony in Measurements and Computations: Theory, Algorithms, and Applications

University of Minnesota Ph.D. dissertation. July 2018. Major: Electrical Engineering. Advisor: Jarvis Haupt. 1 computer file (PDF); xvi, 289 pages.In modern ``Big Data'' applications, structured learning is the most widely employed methodology. Within this paradigm, the fundamental challenge lies in developing practical, effective algorithmic inference methods. Often (e.g., deep learning) successful heuristic-based approaches exist but theoretical studies are far behind, limiting understanding and potential improvements. In other settings (e.g., recommender systems) provably effective algorithmic methods exist, but the sheer sizes of datasets can limit their applicability. This twofold challenge motivates this work on developing new analytical and algorithmic methods for structured learning, with a particular focus on parsimony in measurements and computation, i.e., those requiring low storage and computational costs. Toward this end, we make efforts to investigate the theoretical properties of models and algorithms that present significant improvement in measurement and computation requirement. In particular, we first develop randomized approaches for dimensionality reduction on matrix and tensor data, which allow accurate estimation and inference procedures using significantly smaller data sizes that only depend on the intrinsic dimension (e.g., the rank of matrix/tensor) rather than the ambient ones. Our next effort is to study iterative algorithms for solving high dimensional learning problems, including both convex and nonconvex optimization. Using contemporary analysis techniques, we demonstrate guarantees of iteration complexities that are analogous to the low dimensional cases. In addition, we explore the landscape of nonconvex optimizations that exhibit computational advantages over their convex counterparts and characterize their properties from a general point of view in theory