10286772
Image segmentation by spatially adaptive color and texture features
An image segmentation algorithm that is based on spatially adaptive color and texture features is presented. The proposed algorithm is based on a previously proposed algorithm but introduces a number of new elements. We use a new set of texture features based on a steerable filter decomposition. The steerable filters combined with a new spatial texture segmentation scheme provide a finer and more robust segmentation into texture classes. The proposed algorithm includes an elaborate border estimation procedure, which extends the idea of Pappas (1992) adaptive clustering segmentation algorithm to color texture. The performance of the proposed algorithm is demonstrated in the domain of photographic images, including low resolution compressed images.
971415
Adaptive image segmentation based on color and texture
We propose an image segmentation algorithm that is based on spatially adaptive color and texture features. The features are first developed independently, and then combined to obtain an overall segmentation. Texture feature estimation requires a finite neighborhood which limits the spatial resolution of texture segmentation, while color segmentation provides accurate and precise edge localization. We combine a previously proposed adaptive clustering algorithm for color segmentation with a simple but effective texture segmentation approach to obtain an overall image segmentation. Our focus is in the domain of photographic images with an essentially unlimited range of topics. The images are assumed to be of relatively low resolution and may be degraded or compressed.
7832450
Adaptive perceptual color-texture image segmentation
We propose a new approach for image segmentation that is based on low-level features for color and texture. It is aimed at segmentation of natural scenes, in which the color and texture of each segment does not typically exhibit uniform statistical characteristics. The proposed approach combines knowledge of human perception with an understanding of signal characteristics in order to segment natural scenes into perceptually/semantically uniform regions. The proposed approach is based on two types of spatially adaptive low-level features. The first describes the local color composition in terms of spatially adaptive dominant colors, and the second describes the spatial characteristics of the grayscale component of the texture. Together, they provide a simple and effective characterization of texture that the proposed algorithm uses to obtain robust and, at the same time, accurate and precise segmentations. The resulting segmentations convey semantic information that can be used for content-based retrieval. The performance of the proposed algorithms is demonstrated in the domain of photographic images, including low-resolution, degraded, and compressed images.
8668982
Perceptually-tuned multiscale color-texture segmentation
We present a perceptually-tuned multiscale image segmentation algorithm that is based on spatially adaptive color and texture features. The proposed algorithm extends a previously proposed approach to include multiple texture scales. The determination of the multiscale texture features is based on perceptual considerations. We also examine the perceptual tuning of the algorithm and how it is affected by the presence of different texture scales. The multiscale extension is necessary for segmenting higher resolution images and is particularly effective in segmenting objects shown in different perspectives. The performance of the proposed algorithm is demonstrated in the domain of photographic images.
4989255
Face Detection Based on the Manifold
Data collection for both training and testing a classifier is a tedious but essential step towards face detection and recognition. It is a piece of cake to collect more than hundreds of thousands of examples from web and digital camera nowadays. How to train a face detector based on the collected immense face database? This paper presents a manifold-based method to select a training set. That is to say we learn the manifold from the collected enormous face database and then subsample and interweave the training set by the estimated geodesic distance in the low-dimensional manifold embedding. By the resulting training set, we train an AdaBoost-based face detector. The trained detector is tested on the MIT+CMU frontal face test set. The experimental results show that the proposed method based on the manifold is efficient to train a classifier confronted with the huge database.
2587881
Isomap Based on the Image Euclidean Distance
Scientists find that the human perception is based on the similarity on the manifold of data set. Isometric feature mapping (isomap) is one of the representative techniques of manifold. It is intuitive, well understood and produces reasonable mapping results. However, if the input data for manifold learning are corrupted with noises, the isomap algorithm is topologically unstable. In this paper, we present an improved manifold learning method when the input data are images - the image Euclidean distance based isomap (imisomap), in which we use a new distance for images called image Euclidean distance (IMED). Experimental results demonstrate a consistent performance improvement of the algorithm imisomap over the traditional isomap based on Euclidean distance
2913360
Dynamic verification of temporal constraints on-the-fly for workflow systems
Temporal verification is an important method to check the temporal correctness of workflow management systems (WfMSs). However, the current temporal verification is relatively independent of the workflow system environments. It does not pay sufficient attention to the interrelationship between the temporal verification at different stages, the mutual dependency between some temporal constraints, and the run-time checkpoint selection strategy, which hence hinders the incorporation and consistency between the temporal verification and the workflow system environments. In this paper, we effectively integrate the temporal verification at different stages and explore the dependency between some temporal constraints. In addition, we present a new effective runtime checkpoint selection strategy which dynamically selects appropriate checkpoints based on the activity completion duration. Furthermore, based on these analyses, we develop some new methods for more efficient temporal verification. These analyses and new methods help to eliminate the gap between the temporal verification and the workflow system environments.
20682205
Greedy Localization and Color-Coding: Improved Matching and Packing Algorithms
Matching and packing problems have formed an important class of NP-hard problems. There have been a number of recently developed techniques for parameterized algorithms for these problems, including greedy localization, color-coding plus dynamic programming, and randomized divide-and-conquer. In this paper, we provide further theoretical study on the structures of these problems, and develop improved algorithmic methods that combine existing and new techniques to obtain improved algorithms for matching and packing problems. For the 3-SET PACKING problem, we present a deterministic algorithm of time O*(4.613 k ), which significantly improves the previous best deterministic algorithm of time O* (12.8 3k For the 3-D MATCHING problem, we develop a new randomized algorithm of running time O*(2.32 3k ) and a new deterministic algorithm of running time O* (2.77 3k ). Our randomized algorithm improves the previous best randomized algorithm of running time O*(2.52 3k ), and our deterministic algorithm significantly improves the previous best deterministic algorithm of running time O* (12.8 3k ). Our results also imply improved algorithms for various triangle packing problems in graphs.
30612222
Constrained minimum vertex cover in bipartite graphs: complexity and parameterized algorithms
Motivated by the research in reconfigurable memory array structures, this paper studies the complexity and algorithms for the constrained minimum vertex cover problem on bipartite graphs (NIN-CVCB) defined as follows: given a bipartite graph G = (V, E) with vertex bipartition V = U ∪ L and two integers ku and kl, decide whether there is a minimum vertex cover in G with at most ku vertices in U and at most kl vertices in L. It is proved in this paper that the MIN-CVCB problem is NP-complete. This answers a question posed by Hasan and Liu. A parameterized algorithm is developed for the problem, in which classical results in matching theory and recently developed techniques in parameterized computation theory are nicely combined and extended. The algorithm runs in time O(1.26ku + kl + (ku + kl) |G|) and significantly improves previous algorithms for the problem.