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.
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.
24184742
On Distance Two Labellings of Graphs
2385789
Efficient self-healing group key distribution with revocation capability
This paper presents group key distribution techniques for large and dynamic groups over unreliable channels. The techniques proposed here are based on the self-healing key distribution methods (with revocation capability) recently developed by Staddon et al. [27]. By introducing a novel personal key distribution technique, this paper reduces (1) the communication overhead of personal key share distribution from O(t2log q) to O(tlogq), (2) the communication overhead of self-healing key distribution with t-revocation capability from O((mt2+tm)log q) to O(mtlog q), and (3) the storage overhead of the self-healing key distribution with $t$-revocation capability at each group member from O(m2log q) to O(mlogq), where $t$ is the maximum number of colluding group members, $m$ is the number of sessions, and $q$ is a prime number that is large enough to accommodate a cryptographic key. All these results are achieved without sacrificing the unconditional security of key distribution. In addition, this paper presents two techniques that allow trade-off between the broadcast size and the recoverability of lost session keys. These two methods further reduce the broadcast message size in situations where there are frequent but short-term disruptions of communication and where there are long-term but infrequent disruptions of communication, respectively.
399636
Resilient Cluster Formation for Sensor Networks
Sensor nodes are often organized into clusters to facilitate many network operations such as data aggregation. Clustering has to be protected in hostile environments. Otherwise, an attacker can easily mislead a cluster-based application by attacking the clustering protocol. This paper proposes three techniques for resilient cluster formation. The simple neighbor validation provides a simple yet effective way to validate a sensor's neighbors; the priority-based selection organizes clusters basedon the sensor's priority of being a cluster head; and the centralized detection further enhances the security by detecting misbehaving nodes. Another appealing benefit of this protocol is that a sensor node can make a clustering decision immediately once the neighborhood information is available. This further increases the difficulty of attacking the clustering protocol. The analysis also shows that the proposed protocol is efficient and effective in dealing with attacks.
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.
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.