18107138
A Framework for Web-Based Interactive Applications of High-Resolution 3D Medical Image Data
With the advances in medical imaging devices, large volumes of high-resolution 3D medical image data have been produced. These high-resolution 3D data are very large in size, and severely stress storage systems and networks. Most existing Web-based 3D medical image interactive applications therefore deal with only low- or medium-resolution image data. While it is possible to download the whole 3D high-resolution image data from the server and perform the image visualization and analysis at the client site, such an alternative is infeasible when the high-resolution data are very huge, and many users concurrently access the server. In this paper, we propose a novel framework for Web-based interactive applications of high-resolution 3D medical image data. Specifically, we first partition the whole 3D data into buckets, and then compress each bucket separately. We also propose an indexing structure for these buckets to efficiently support typical queries such as 3D slicer and region of interest (ROI), and only the relevant buckets are transmitted instead of the whole high-resolution 3D medical image data. Furthermore, in order to better support concurrent accesses and to improve the average response time, we also propose some techniques for bucket group access on the server side and incremental transmission. Our experimental study based on a human brain MRI data set indicates that the proposed framework can significantly reduce storage and communication requirements, and can enable real-time interaction with remote high-resolution 3D medical image data for many concurrent users
694856
Support Concurrent Queries in Multiuser CBIR Systems
Various techniques have been developed for different query types in content-based image retrieval (CBIR) systems such as sampling queries, constrained sampling queries, multiple constrained sampling queries, k-NN queries, constrained k-NN queries, and multiple localized k-NN queries. In this paper, we propose a generalized query model suitable for expressing queries of different types, and investigate efficient processing techniques for this new framework. We develop new storage and query processing techniques to exploit sequential access and leverage inter-query concurrency to share computation. Our experimental results, based on the Corel dataset, indicates that the proposed optimization can significantly reduce average response time in a multiuser environment.
14999533
Efficient target search with relevance feedback for large CBIR systems
Recent content-based image retrieval (CBIR) techniques were designed around query refinement based on relevance feedback. They suffer from slow convergence, high disk I/O, and do not even guarantee to find intended targets. In this paper, we identify the cause of these problems and propose several efficient target search methods to address these drawbacks. Our complexity analysis shows that our approach is able to reach any given target image with fewer iterations in the worst and average cases. We evaluated our techniques on large datasets in simulated and realistic environments. The results show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be used to improve the effectiveness of existing CBIR systems with relevance feedback.
8780795
Fast Query Point Movement Techniques with Relevance Feedback for Content-Based Image Retrieval
Target search in content-based image retrieval (CBIR) systems refers to finding a specific (target) image such as a particular registered logo or a specific historical photograph. Existing techniques were designed around query refinement based on relevance feedback, suffer from slow convergence, and do not even guarantee to find intended targets. To address those limitations, we propose several efficient query point movement methods. We theoretically prove that our approach is able to reach any given target image with fewer iterations in the worst and average cases. Extensive experiments in simulated and realistic environments show that our approach significantly reduces the number of iterations and improves overall retrieval performance. The experiments also confirm that our approach can always retrieve intended targets even with poor selection of initial query points and can be employed to improve the effectiveness and efficiency of existing CBIR systems.
6586245
Reconstructing XML Subtrees from Relational Storage of XML documents
Numerous researchers have proposed to use relational databases to store and query XML documents. One important component of such systems is the XML subtree reconstruction, which reconstructs the subtrees rooted at the matching nodes of an XML query and returns them to the user as the query result. Existing reconstruction algorithms either do not support recursive XML view schema, or require expensive nested queries or joins of multiple relations. In this paper, we propose an efficient XML subtree reconstruction algorithm, Reconstruct, which overcomes these limitations and uses an efficient stack-based structural join algorithm to recover all the parent-child relationships between elements. One salient advantage of this algorithm is that it employs the inlining feature of the inlining-based storage of XML documents, which is known as one of the best relational XML storage schemes. Both our algorithmic analysis and experimental study show that Reconstruct is efficient and scalable.
41350051
Language engineering for the Semantic Web: a digital library for endangered languages
31887438
Robust Stability Analysis of a Class of Hopfield Neural Networks with Multiple Delays
The robust stability of a class of Hopfield neural networks with multiple delays is analyzed. Sufficient conditions for the global robust stability of the equilibrium point are established through constructing a suitable Lyapunov-Krasovskii functional. The present results take the form of linear matrix inequalities, and are computationally efficient. In addition, the results are independent of delays and established without assuming differentiability and monotonicity of the activation function.
12777678
Unsupervised Image Layout Extraction
We propose a novel unsupervised learning algorithm to extract the layout of an image by learning latent object-related aspects. Unlike traditional image segmentation algorithms that segment an image using feature similarity, our method is able to learn high-level object characteristics (aspects) from a large number of unlabeled images containing similar objects to facilitate image segmentation. Our method does not require human to annotate the training set and works without supervision. We use a graphical model to address the learning of aspects and layout extraction together. In particular, aspect-feature dependency from multiple images is learned via the expectation-maximization algorithm. We demonstrate that, by associating latent aspects to spatial structure, the proposed method achieves much better layout extraction results than using probabilistic latent semantic analysis.
5968490
Attack-resistant location estimation in sensor networks
Many sensor network applications require sensors' locations to function correctly. Despite the recent advances, location discovery for sensor networks in hostile environments has been mostly overlooked. Most of the existing localization protocols for sensor networks are vulnerable in hostile environments. The security of location discovery can certainly be enhanced by authentication. However, the possible node compromises and the fact that location determination uses certain physical features (e.g., received signal strength) of radio signals make authentication not as effective as in traditional security applications. This paper presents two methods to tolerate malicious attacks against beacon-based location discovery in sensor networks. The first method filters out malicious beacon signals on the basis of the "consistency" among multiple beacon signals, while the second method tolerates malicious beacon signals by adopting an it era lively refined voting scheme. Both methods can survive malicious attacks even if the attacks bypass authentication, provided that the benign beacon signals constitute the majority of the "consistent" beacon signals. This paper also presents the implementation of these techniques on MICA2 motes running TinyOS, and the evaluation through both simulation and field experiments. The experimental results demonstrate that the proposed methods are promising for the current generation of sensor networks.