2846430
Super-resolution through neighbor embedding
In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its high-resolution counterpart using a set of training examples. While this formulation resembles other learning-based methods for super-resolution, our method has been inspired by recent manifold teaming methods, particularly locally linear embedding (LLE). Specifically, small image patches in the lowand high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is very flexible and gives good empirical results.
2308494
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 clustering 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 similarity measure for spectral clustering. Our method is significantly more robust than spectral clustering and path-based clustering. We have performed experiments based on both synthetic and real-world data, comparing our method with some other methods. In 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.
1730596
A Scalable Kernel-Based Algorithm for Semi-Supervised Metric Learning
In recent years, metric learning in the semisupervised setting has aroused a lot of research interests. One type of semi-supervised metric learning utilizes supervisory information in the form of pairwise similarity or dissimilarity constraints. However, most methods proposed so far are either limited to linear metric learning or unable to scale up well with the data set size. In this paper, we propose a nonlinear metric learning method based on the kernel approach. By applying low-rank approximation to the kernel matrix, our method can handle significantly larger data sets. Moreover, our low-rank approximation scheme can naturally lead to out-of-sample generalization. Experiments performed on both artificial and real-world data show very promising results.
4841642
Locally linear metric adaptation with application to semi-supervised clustering and image retrieval
Many computer vision and pattern recognition algorithms are very sensitive to the choice of an appropriate distance metric. Some recent research sought to address a variant of the conventional clustering problem called semi-supervised clustering, which performs clustering in the presence of some background knowledge or supervisory information expressed as pairwise similarity or dissimilarity constraints. However, existing metric learning methods for semi-supervised clustering mostly perform global metric learning through a linear transformation. In this paper, we propose a new metric learning method that performs nonlinear transformation globally but linear transformation locally. In particular, we formulate the learning problem as an optimization problem and present three methods for solving it. Through some toy data sets, we show empirically that our locally linear metric adaptation (LLMA) method can handle some difficult cases that cannot be handled satisfactorily by previous methods. We also demonstrate the effectiveness of our method on some UCI data sets. Besides applying LLMA to semi-supervised clustering, we have also used it to improve the performance of content-based image retrieval systems through metric learning. Experimental results based on two real-world image databases show that LLMA significantly outperforms other methods in boosting the image retrieval performance.
8605759
Fine-grained contract negotiation for hierarchical software components
Component-based development addresses the complexity of large applications by building software systems from reusable software components. To support reuse and successfully combine units of software, the contractual approach turns out to be well-suited to specify and to verify components and their interactions. However, as contracts must support functional and extra-functional aspects, they are frequently challenged by fluctuations in extra-functional properties and dynamic reconfigurations of components. In this paper, we propose a negotiation model in which components have clearly identified roles and interact in order to automatically restore the validity of contracts. The negotiation model currently supports a concession-based negotiation policy and is well-suited to behavioral contracts based on executable assertions. This model is integrated into ConFract, a contracting system for the fractal hierarchical component model.
11911656
802.11 Link Interference: A Simple Model and A Performance Enhancement
Recent findings have revealed that the carrier-sensing range set in current major implementations does not detect all interference signals in 802.11 networks. In this paper, we investigate the effect of interference and develop a mathematical model for it. The accuracy of our model is verified via simulations. Based on an insight from our model, we present a simple protocol that operates on the top of 802.11 MAC layer and achieves more throughput than rate-adjustment schemes.
25776373
Iteration-free fractal image coding based on efficient domain pool design
The domain pool design is one of the dominant issues which affect the coding performance of fractal image compression. In this paper, we employ the LBG algorithm and propose a block averaging method to design the efficient domain pools based on a proposed iteration-free fractal image codec. The redundancies between the generated domain blocks are reduced by the proposed methods. Therefore, we can obtain the domain pools that are more efficient than those in the conventional fractal coding schemes and thus the coding performance is improved. On the other hand, the iteration process in the conventional fractal coding scheme not only requires a large size of memory and a high computation complexity but also prolongs the decoding process. The proposed iteration-free fractal codec can overcome the problems above. In computer simulation, both the LBG-based and block-averaging methods for the domain pool design in the proposed iteration free scheme achieve excellent performances. For example, based on the proposed block-averaging method, the decoded Lena image has at least a 0.5 dB higher PSNR (under the same bit rate) and an eight-time faster decoding speed than the conventional fractal coding schemes that require iterations.
32579362
Tele-action objects for an active multimedia system
We present the concept of tele-action objects (TAOs), which possess private knowledge specific to the object instances. The user can create and modify the private knowledge of a tele-action object, so that the tele-action object will automatically react to certain events to pre-perform operations for generating timely response, improving operational efficiency and maintaining consistency. Moreover tele-action objects also possess a hypergraph structure leading to the effective presentation and efficient communication of multimedia information. The active multimedia system (AMS) is provided to manipulate and maintain the TAOs. The user interface and the system architecture for the AMS are described. A multimedia mail system is implemented to illustrate the usefulness of tele-action objects. Finally, we discuss the advantages of the proposed approach and future research.
35414635
Cache-Aware Real-Time Disk Scheduling
Previous real-time disk scheduling algorithms assume that each disk request incurs a physical disk mechanical operation and only consider how to move the disk head under real-time constraints. However, with the increased capacity of on-disk cache, modern disk drives read-ahead data aggressively. Thus, the on-disk cache may service many disk requests without incurring physical disk access. By exploring the design methodology of on-disk cache, in this paper, we propose cacheaware real-time disk scheduling algorithms that take the on-disk cache into consideration during scheduling. Therefore, the scheduling algorithm can help the cache replacement scheme to minimize the cache miss ratio. Besides, the service timing estimation is more accurate in schedulability analysis since the cache effect is considered during scheduling. A simulation-based evaluation shows the proposed scheduling algorithms to be highly successful as compared with the classical real-time disk scheduling algorithms. For example, under sequential workload with 10 sequential streams, the data throughput of our scheme is 1.1 times that of DM-SCAN.