8902172
Kernel-based distance metric learning for content-based image retrieval
For a specific set of features chosen for representing images, the performance of a content-based image retrieval (CBIR) system depends critically on the similarity or dissimilarity measure used. Instead of manually choosing a distance function in advance, a more promising approach is to learn a good distance function from data automatically. In this paper, we propose a kernel approach to improve the retrieval performance of CBIR systems by learning a distance metric based on pairwise constraints between images as supervisory information. Unlike most existing metric learning methods which learn a Mahalanobis metric corresponding to performing linear transformation in the original image space, we define the transformation in the kernel-induced feature space which is nonlinearly related to the image space. Experiments performed on two real-world image databases show that our method not only improves the retrieval performance of Euclidean distance without distance learning, but it also outperforms other distance learning methods significantly due to its higher flexibility in metric learning.
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.
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.
16045709
A General Model and Analysis of Physical Layer Capture in 802.11 Networks
While packet capture has been observed in real implementations of 802.11 devices, there is a lack of accurate models that describe the phenomenon. We present a general analytical model and an iterative method that predicts error probabilities and throughputs of packet transmissions with multiple senderreceiver pairs. Our model offers a more accurate prediction than previous work by taking into account the cumulative strength of interference signals and using the BER model to convert a signal to interference and noise ratio value to a bit error probability. This permits the analysis of packet reception at any transmission rate with interference from neighbors at any set of locations. We also prove that our iterative method converges, and we verify the accuracy of our model through simulations in Qualnet. Last, we present a rate assignment algorithm to reduce the average delay as an application of our analysis.
15864534
Real-Time Disk Scheduling with On-Disk Cache Conscious
Previous real-time disk scheduling algorithms assume that each disk request incurs a 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 lots of requests without incurring physical disk access. By exploring the design methodology of on-disk cache, in this paper, we propose CARDS: a cache-aware real-time disk scheduling algorithm that takes the on-disk cache into consideration during scheduling. Therefore, the scheduling algorithm can help to minimize the cache miss ratio. Besides, the service timing estimation is more accurate in schedulability analysis since the cache effect is considered. A simulation-based evaluation shows CARDS to be highly successful as compared to the classical real-time disk scheduling algorithms. For example, under sequential workload with 10 sequential streams, the data throughput of CARDS is 1.1 times of DM-SCAN.
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.
1945331
From Components to Autonomic Elements Using Negotiable Contracts
Autonomic computing aims at producing software systems that can manage themselves. As component-based development also partly addresses the complexity of large applications, we propose to combine the benefits of both approaches by using components equipped with negotiable contracts. These contracts specify the correct behavior of the components and play a central role in feedback control loops to enforce some autonomic features on components. In this paper, we present ConFract, a contract-based framework for hierarchical components in which contracts are runtime objects that are dynamically built from specifications, and automatically updated according to dynamic reconfigurations. Moreover, contracts clearly define the responsibilities (guarantor, beneficiaries) between their participating components which are exploited by some negotiations to automatically adapt components and contracts, and revalidate the system. The generic negotiation mechanism and an associated concession-based policy are presented.
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.