Content-aware search of multimedia data in ad hoc networks
MSWiM '05 Proceedings of the 8th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
Similarity-based clustering strategy for mobile ad hoc multimedia databases
Mobile Information Systems
Knowledge-based image retrieval system
Knowledge-Based Systems
An Architectural Paradigm for Collaborative Semantic Indexing of Multimedia Data Objects
VISUAL '08 Proceedings of the 10th international conference on Visual Information Systems: Web-Based Visual Information Search and Management
Collective Evolutionary Indexing of Multimedia Objects
ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part I
Hierarchical semantic-based index for ad hoc image retrieval
Journal of Mobile Multimedia
Location-Aware Caching for Semantic-Based Image Queries in Mobile AD HOC Networks
International Journal of Multimedia Data Engineering & Management
Semantic Image Retrieval Using Collaborative Indexing and Filtering
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
| Hi-index | 0.00 |
Content-based image retrieval is a challenging problem in mobile ad hoc networks due to the multiple limitations such as network bandwidth, infrastructure-free nature, and node mobility. The traditional systems employ either centralized or flooding strategies, which may result in low fault tolerance or high search cost. In this paper, we propose a decentralized non-flooding retrieval scheme in multi-hop ad hoc networks 驴 Semantic Ad hoc Image Retrieval (SAIR). The novelty of SAIR stems from several factors including: (1) representation of image contents using first-order logic expressions; (2) clustering mobile nodes based on their data contents; and (3) performing content-based image retrieval within a reduced scope of mobile nodes. Through extensive simulations, we show that relative to the flooding strategy, SAIR can retrieve the semantically most similar image objects by accessing only a small portion of the mobile nodes with lower search cost. Moreover, it is scalable to large network sizes and large number of data objects.