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.
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.
45957444
An Adaptive Image Segmentation Method Based on a Modified Pulse Coupled Neural Network
Pulse-coupled neural network (PCNN) based on Eckhorn's model of the cat visual cortex has great significant advantage in image segmentation. However, the segmented performance depends on the suitable PCNN parameters, which are tuned by trial so far. Focusing on the famous difficult problem of PCNN, this paper establishes a modified PCNN, and proposes adaptive PCNN parameters determination algorithm based on water region area. Experimental results on image segmentation demonstrate its validity and robustness.
11674907
An Analytic Model Predicting the Optimal Range for Maximizing 1-Hop Broadcast Coverage in Dense Wireless Networks
We present an analytic model to predict the optimal range for maximizing 1-hop broadcast coverage in dense ad-hoc wireless networks, using information like network density and node sending rate. We first derive a geometric-based, probabilistic model that describes the expected coverage as a function of range, sending rate and density. Because we can only solve the resulting equations numerically, we next develop extrapolations that find the optimum range for any rate and density given a single precomputed optimum. Finally using simulation, we show that in spite of many simplifications in the model, our extrapolation is able to predict the optimal range to within 16%.
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.
17810621
Enhancing Relevance Models with Adaptive Passage Retrieval
Passage retrieval and pseudo relevance feedback/query expansion have been reported as two effective means for improving document retrieval in literature. Relevance models, while improving retrieval in most cases, hurts performance on some heterogeneous collections. Previous research has shown that combining passage-level evidence with pseudo relevance feedback brings added benefits. In this paper, we study passage retrieval with relevance models in the language-modeling framework for document retrieval. An adaptive passage retrieval approach is proposed to document ranking based on the best passage of a document given a query. The proposed passage ranking method is applied to two relevance-based language models: the Lavrenko-Croft relevance model and our robust relevance model. Experiments are carried out with three query sets on three different collections from TREC. Our experimental results show that combining adaptive passage retrieval with relevance models (particularly the robust relevance model) consistently outperforms solely applying relevance models on full-length document retrieval.