163750
Query Decomposition: A Multiple Neighborhood Approach to Relevance Feedback Processing in Content-based Image Retrieval
Today’s Content-Based Image Retrieval (CBIR) techniques are based on the "k-nearest neighbors" (k- NN) model. They retrieve images from a single neighborhood using low-level visual features. In this model, semantically similar images are assumed to be clustered in the high-dimensional feature space. Unfortunately, no visual-based feature vector is sufficient to facilitate perfect semantic clustering; and semantically similar images with different appearances are always clustered into distinct neighborhoods in the feature space. Confinement of the search results to a single neighborhood is an inherent limitation of the k-NN techniques. In this paper we consider a new image retrieval paradigm — the Query Decomposition model - that facilitates retrieval of semantically similar images from multiple neighborhoods in the feature space. The retrieval results are the k most similar images from different relevant clusters. We introduce a prototype, and present experimental results to illustrate the effectiveness and efficiency of this new approach to content-based image retrieval.
14012169
Efficient k nearest neighbor queries on remote spatial databases using range estimation
We propose a k-NN query processing algorithm that uses one or more window queries to retrieve the nearest neighbors of a given query point. We also propose two different methods to estimate the ranges to be used by the window queries. Each range estimation method requires different statistical knowledge about the spatial databases. Our experiments on the TIGER data allow us to study the behavior of the proposed algorithm using different range estimation methods. Apart from not requiring direct access to the spatial indices, the window queries used in the proposed algorithm can be easily supported by non-spatial database systems containing spatial objects.
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.
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.
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.
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
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.
8564766
Location-based pairwise key establishments for static sensor networks
Sensor networks are ideal candidates for applications such as target tracking and environment monitoring. Security in sensor networks is critical when there are potential adversaries. Establishment of pairwise keys is a fundamental security service, which forms the basis of other security services such as authentication and encryption. However, establishing pairwise keys in sensor networks is not a trivial task, particularly due to the resource constraints on sensors. This paper presents several techniques for establishing pairwise keys in static sensor networks. These techniques take advantage of the observation that in static sensor networks, although it is difficult to precisely pinpoint sensors' positions, it is often possible to approximately determine their locations. This paper presents a simple location-aware deployment model, and develops two pairwise key predistribution schemes, a closest pairwise keys predistribution scheme and a location-based pairwise keys scheme using bivariate polynomials, by taking advantage of sensors' expected locations. The analysis in this paper indicates that these schemes can achieve better performance if such location information is available and that the smaller the deployment error (i.e., the difference between a sensor's actual location and its expected location) is, the better performance they can achieve.
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.
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.
7771298
Robust stability of a class of nonlinear time-varying discrete systems
This paper studies the problem of robust absolute stability of a class of nonlinear discrete-time systems with time-varying matrix uncertainties of polyhedral type and multiple time-varying sector nonlinearities. By using the variational method and the Lyapunov Second Method, criteria for robust absolute stability are obtained in different forms for the given class of systems. Specifically, we determine the parametric class of Lyapunov functions which defines the necessary and sufficient conditions of robust absolute stability. We apply these Lyapunov functions to derive an algebraic criterion for robust absolute stability in the form of solvability conditions of a set of matrix equations.