15133148
Modeling Image Data for Effective Indexing and Retrieval in Large General Image Databases
In this paper, we propose an image semantic model based on the knowledge and criteria in the field of linguistics and taxonomy. Our work bridges the "semantic gap" by seamlessly exploiting the synergy of both visual feature processing and semantic relevance computation in a new way, and provides improved query efficiency and effectiveness for large general image databases. Our main contributions are as follows: we design novel data structures, namely, a lexical hierarchy, an image-semantic hierarchy, and a number of atomic semantic domains, to capture the semantics and the features of the database, and to provide the indexing scheme. We present a novel image query algorithm based on the proposed structures. In addition, we propose a novel term expansion mechanism to improve the lexical processing. Our extensive experiments indicate that our proposed techniques are effective in achieving high runtime performance with improved retrieval accuracy. The experiments also show that the proposed method has good scalability.
25240485
An Image-Semantic Ontological Framework for Large Image Databases
In the past decade, the Internet has rapidly become a most prevalent platform for information sharing and data communications all over the world. This trend has been enhanced by the wide proliferation of home-used digital photos and videos. Although the Web has seen numerous applications for photo storage, sharing, and searching, few image retrieval systems provide satisfactory search service. The so-called semantic gap and curse of dimensionality are two major barriers that existing technologies cannot adequately address. In this paper, we propose a framework to achieve effective and efficient content-base retrieval on images by addressing the above two issues. That is, we need to narrow the semantic gap while mitigating the curse of dimensionality.
16964451
HISA: a query system bridging the semantic gap for large image databases
We propose a novel system called HISA for organizing very large image databases. HISA implements the first known data structure to capture both the ontological knowledge and visual features for effective and effcient retrieval of images by either keywords, image examples, or both. HISA employs automatic image annotation technique, ontology analysis and statistical analysis of domain knowledge to precompute the data structure. Using these techniques, HISA is able to bridge the gap between the image semantics and the visual features, therefore providing more user-friendly and high-performance queries. We demonstrate the novel data structure employed by HISA, the query algorithms, and the pre-computation process.
35636017
A Novel Pixel-Level and Feature-Level Combined Multisensor Image Fusion Scheme
This paper proposes a novel image fusion scheme, which combines the merits of pixel-level and feature-level fusion algorithms. It avoids some of the well-known problems in pixel-level fusion such as blurring effects and high sensitivity to noise and misregistration. The algorithm first segments images into several regions, then extract features from each segmented region to get the fused image. Two typical image segmentation methods, region growing and edge detection based are both presented in this paper. Experimental results have demonstrated that the proposed method has extensive application scope and it outperforms the multiscale decompositions (MSD) based fusion approaches, both in visual effect and objective evaluation criteria.
61405273
Using adaptive range control to maximize 1-hop broadcast coverage in dense wireless networks
We present a distributed algorithm for maximizing 1-hop broadcast coverage in dense wireless sensor networks. Our strategy is built upon an analytic model that predicts the optimal range for maximizing 1-hop broadcast coverage given information like network density and node sending rate. The algorithm allows each node to set the maximizing radio range using only the locally observed sending rate and node density. The algorithm is thus critically dependent on the empirical determination of these parameters. Our algorithm can observe the parameters using only message eavesdropping and thus does not require extra protocol messages. Using simulation, we show that in spite of many simplifications in the model and incomplete density information in a live network, our algorithm converges fairly quickly and provides good coverage for both uniform and non-uniform networks across a wide range of conditions. We also demonstrate the utility of our algorithm for higher layer protocols by showing that it significantly improves the reception rate for a flooding application as well as the performance of a localization protocol.
13231021
Optimized Grey RBF Prediction Model Based on Genetic Algorithm
When combining grey system with RBF neural network, local optimization and convergence problems are still existed, so genetic algorithm is introduced to assist the modeling of grey neural network in this paper. Firstly, genetic algorithm is employed to solve the parameters of improved GM(1,1) with Lagrange's value theorem, and then RBF neural network is parallel connected to compensate errors. A new dynamic prediction model integrating genetic algorithm and grey RBF, for short GA-GRBF is proposed. This new model with preferable structure and parameters is applied to simulation and analysis of time-displacement data of wind response. The comparative experiment results show that this model is capable of predicting a small sample of data accurately, easily and conveniently.
16418153
The limits of localization using RSS
We characterize the fundamental limits of localization using signal strength in indoor environments. Signal strength approaches are attractive because they are widely applicable to wireless sensor networks and do not require additional localization hardware. We show that although a broad spectrum of algorithms can trade accuracy for precision, none has a significant advantage in localization performance. We found that using commodity 802.11 technology over a range of algorithms, approaches and environments, one can expect a median localization error of 10ft and 97th percentile of 30ft. We present strong evidence that these limitations are fundamental and that they are unlikely to be transcended without fundamentally more complex environmental models or additional localization infrastructure.
16393547
A new robust relevance model in the language model framework
In this paper, a new robust relevance model is proposed that can be applied to both pseudo and true relevance feedback in the language-modeling framework for document retrieval. There are at least three main differences between our new relevance model and other relevance models. The proposed model brings back the original query into the relevance model by treating it as a short, special document, in addition to a number of top-ranked documents returned from the first round retrieval for pseudo feedback, or a number of relevant documents for true relevance feedback. Second, instead of using a uniform prior as in the original relevance model proposed by Lavrenko and Croft, documents are assigned with different priors according to their lengths (in terms) and ranks in the first round retrieval. Third, the probability of a term in the relevance model is further adjusted by its probability in a background language model. In both pseudo and true relevance cases, we have compared the performance of our model to that of the two baselines: the original relevance model and a linear combination model. Our experimental results show that the proposed new model outperforms both of the two baselines in terms of mean average precision.