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.
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.
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.
16699960
Circular chromatic number for iterated Mycielski graphs
Abstract For a graph G , let M( G ) denote the Mycielski graph of G . The t th iterated Mycielski graph of G , M t ( G ), is defined recursively by M 0 ( G )= G , and M t ( G )=M(M t −1 ( G )) for t ⩾1. Let χ c ( G ) denote the circular chromatic number of G . We prove two main results: (1) If G has a universal vertex x , then χ c (M( G ))= χ (M( G )) if χ c (G−x)>χ(G)− 1 2 and G is not a star, otherwise χ c ( M (G))=χ( M (G))− 1 2 ; and (2) χ c (M t ( K m ))= χ (M t ( K m )) if m ⩾2 t −1 +2 t −2 and t ⩾2.
12123652
Analysis of blocking dynamic circuits
In order for dynamic circuits to operate correctly, their inputs must be monotonically rising during evaluation. Blocking dynamic circuits satisfy this constraint by delaying evaluation until all inputs have been properly setup relative to the evaluation clock. By viewing dynamic gates as latches, we demonstrate that the optimal delay of a blocking dynamic gate may occur when the setup time is negative. With blocking dynamic circuits, cascading low-skew dynamic gates allows each dynamic gate to tolerate a degraded input level. The larger noise margin provides greater flexibility with the delay vs. noise margin trade-off (i.e. the circuit robustness vs. speed tradeoff). This paper generalizes blocking dynamic circuits and provides a systematic approach for assigning clock phases, given delay and noise margin constraints. Using this framework, one can analyze any logic network consisting of blocking dynamic circuits.
12746246
JRV: an interactive tool for data mining visualization
In this paper, we demonstrate JRV, a new data mining visualization tool for the knowledge discovery process where the user and computer can cooperate with each other. First, the computer can be instructed by the user interactively to compute values of several evaluation functions. Then, the user can take advantage of domain knowledge and assess the intermediate results obtained. Furthermore, by providing effective and efficient data visualization, the pattern recognition capacities of users can be greatly improved. Instead of being limited to two attributes at a given time in independence diagrams, this novel tool will allow simultaneous analyses of multiple attribute dependencies using four different drawing panels. Also, by utilizing the existing techniques of data visualization, we design a general model which can handle both categorical and numerical attributes in an intuitive way. With this model, we can identify patterns of interests efficiently. Through actual examples, we show that it might help users to find novel attribute relationships. This work is supported by NIH grant #RO1-CA98932-01.
1435851
Soft shape context for iterative closest point registration
This paper introduces a shape descriptor, the soft shape context, motivated by the shape context method. Unlike the original shape context method, where each image point was hard assigned into a single histogram bin, we instead allow each image point to contribute to multiple bins, hence more robust to distortions. The soft shape context can easily be integrated into the iterative closest point (ICP) method as an auxiliary feature vector, enriching the representation of an image point from spatial information only, to spatial and shape information. This yields a registration method more robust than the original ICP method. The method is general for 2D shapes. It does not calculate derivatives, hence being able to handle shapes with junctions and discontinuities. We present experimental results to demonstrate the robustness compared with the standard ICP method.