42,006 research outputs found
SVS-JOIN : efficient spatial visual similarity join for geo-multimedia
In the big data era, massive amount of multimedia data with geo-tags has been generated and collected by smart devices equipped with mobile communications module and position sensor module. This trend has put forward higher request on large-scale geo-multimedia retrieval. Spatial similarity join is one of the significant problems in the area of spatial database. Previous works focused on spatial textual document search problem, rather than geo-multimedia retrieval. In this paper, we investigate a novel geo-multimedia retrieval paradigm named spatial visual similarity join (SVS-JOIN for short), which aims to search similar geo-image pairs in both aspects of geo-location and visual content. Firstly, the definition of SVS-JOIN is proposed and then we present the geographical similarity and visual similarity measurement. Inspired by the approach for textual similarity join, we develop an algorithm named SVS-JOIN B by combining the PPJOIN algorithm and visual similarity. Besides, an extension of it named SVS-JOIN G is developed, which utilizes spatial grid strategy to improve the search efficiency. To further speed up the search, a novel approach called SVS-JOIN Q is carefully designed, in which a quadtree and a global inverted index are employed. Comprehensive experiments are conducted on two geo-image datasets and the results demonstrate that our solution can address the SVS-JOIN problem effectively and efficiently
SEARS: Space Efficient And Reliable Storage System in the Cloud
Today's cloud storage services must offer storage reliability and fast data
retrieval for large amount of data without sacrificing storage cost. We present
SEARS, a cloud-based storage system which integrates erasure coding and data
deduplication to support efficient and reliable data storage with fast user
response time. With proper association of data to storage server clusters,
SEARS provides flexible mixing of different configurations, suitable for
real-time and archival applications.
Our prototype implementation of SEARS over Amazon EC2 shows that it
outperforms existing storage systems in storage efficiency and file retrieval
time. For 3 MB files, SEARS delivers retrieval time of s compared to
s with existing systems.Comment: 4 pages, IEEE LCN 201
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Roadmap on optical security
Postprint (author's final draft
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search
Mobile landmark search (MLS) recently receives increasing attention for its
great practical values. However, it still remains unsolved due to two important
challenges. One is high bandwidth consumption of query transmission, and the
other is the huge visual variations of query images sent from mobile devices.
In this paper, we propose a novel hashing scheme, named as canonical view based
discrete multi-modal hashing (CV-DMH), to handle these problems via a novel
three-stage learning procedure. First, a submodular function is designed to
measure visual representativeness and redundancy of a view set. With it,
canonical views, which capture key visual appearances of landmark with limited
redundancy, are efficiently discovered with an iterative mining strategy.
Second, multi-modal sparse coding is applied to transform visual features from
multiple modalities into an intermediate representation. It can robustly and
adaptively characterize visual contents of varied landmark images with certain
canonical views. Finally, compact binary codes are learned on intermediate
representation within a tailored discrete binary embedding model which
preserves visual relations of images measured with canonical views and removes
the involved noises. In this part, we develop a new augmented Lagrangian
multiplier (ALM) based optimization method to directly solve the discrete
binary codes. We can not only explicitly deal with the discrete constraint, but
also consider the bit-uncorrelated constraint and balance constraint together.
Experiments on real world landmark datasets demonstrate the superior
performance of CV-DMH over several state-of-the-art methods
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