1,519 research outputs found
When images work faster than words: The integration of content-based image retrieval with the Northumbria Watermark Archive
Information on the manufacture, history, provenance, identification, care and conservation of paper-based artwork/objects is disparate and not always readily available. The Northumbria Watermark Archive will incorporate such material into a database, which will be made freely available on the Internet providing an invaluable resource for conservation, research and education. The efficiency of a database is highly dependant on its search mechanism. Text based mechanisms are frequently ineffective when a range of descriptive terminologies might be used i.e. when describing images or translating from foreign languages. In such cases a Content Based Image Retrieval (CBIR) system can be more effective. Watermarks provide paper with unique visual identification characteristics and have been used to provide a point of entry to the archive that is more efficient and effective than a text based search mechanism. The research carried out has the potential to be applied to any numerically large collection of images with distinctive features of colour, shape or texture i.e. coins, architectural features, picture frame profiles, hallmarks, Japanese artists stamps etc. Although the establishment of an electronic archive incorporating a CBIR system can undoubtedly improve access to large collections of images and related data, the development is rarely trouble free. This paper discusses some of the issues that must be considered i.e. collaboration between disciplines; project management; copying and digitising objects; content based image retrieval; the Northumbria Watermark Archive; the use of standardised terminology within a database as well as copyright issues
Exploration of applying a theory-based user classification model to inform personalised content-based image retrieval system design
© ACM, 2016. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published at http://dl.acm.org/citation.cfm?id=2903636To better understand users and create more personalised search experiences, a number of user models have been developed, usually based on different theories or empirical data study. After developing the user models, it is important to effectively utilise them in the design, development and evaluation of search systems to improve users’ overall search experiences. However there is a lack of research has been done on the utilisation of the user models especially theory-based models, because of the challenges on the utilization methodologies when applying the model to different search systems. This paper explores and states how to apply an Information Foraging Theory (IFT) based user classification model called ISE to effectively identify user’s search characteristics and create user groups, based on an empirically-driven methodology for content-based image retrieval (CBIR) systems and how the preferences of different user types inform the personalized design of the CBIR systems
A Benchmark for Image Retrieval using Distributed Systems over the Internet: BIRDS-I
The performance of CBIR algorithms is usually measured on an isolated
workstation. In a real-world environment the algorithms would only constitute a
minor component among the many interacting components. The Internet
dramati-cally changes many of the usual assumptions about measuring CBIR
performance. Any CBIR benchmark should be designed from a networked systems
standpoint. These benchmarks typically introduce communication overhead because
the real systems they model are distributed applications. We present our
implementation of a client/server benchmark called BIRDS-I to measure image
retrieval performance over the Internet. It has been designed with the trend
toward the use of small personalized wireless systems in mind. Web-based CBIR
implies the use of heteroge-neous image sets, imposing certain constraints on
how the images are organized and the type of performance metrics applicable.
BIRDS-I only requires controlled human intervention for the compilation of the
image collection and none for the generation of ground truth in the measurement
of retrieval accuracy. Benchmark image collections need to be evolved
incrementally toward the storage of millions of images and that scaleup can
only be achieved through the use of computer-aided compilation. Finally, our
scoring metric introduces a tightly optimized image-ranking window.Comment: 24 pages, To appear in the Proc. SPIE Internet Imaging Conference
200
Hybrid Information Retrieval Model For Web Images
The Bing Bang of the Internet in the early 90's increased dramatically the
number of images being distributed and shared over the web. As a result, image
information retrieval systems were developed to index and retrieve image files
spread over the Internet. Most of these systems are keyword-based which search
for images based on their textual metadata; and thus, they are imprecise as it
is vague to describe an image with a human language. Besides, there exist the
content-based image retrieval systems which search for images based on their
visual information. However, content-based type systems are still immature and
not that effective as they suffer from low retrieval recall/precision rate.
This paper proposes a new hybrid image information retrieval model for indexing
and retrieving web images published in HTML documents. The distinguishing mark
of the proposed model is that it is based on both graphical content and textual
metadata. The graphical content is denoted by color features and color
histogram of the image; while textual metadata are denoted by the terms that
surround the image in the HTML document, more particularly, the terms that
appear in the tags p, h1, and h2, in addition to the terms that appear in the
image's alt attribute, filename, and class-label. Moreover, this paper presents
a new term weighting scheme called VTF-IDF short for Variable Term
Frequency-Inverse Document Frequency which unlike traditional schemes, it
exploits the HTML tag structure and assigns an extra bonus weight for terms
that appear within certain particular HTML tags that are correlated to the
semantics of the image. Experiments conducted to evaluate the proposed IR model
showed a high retrieval precision rate that outpaced other current models.Comment: LACSC - Lebanese Association for Computational Sciences,
http://www.lacsc.org/; International Journal of Computer Science & Emerging
Technologies (IJCSET), Vol. 3, No. 1, February 201
Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database
In this paper we present a novel architecture for storing visual data.
Effective storing, browsing and searching collections of images is one of the
most important challenges of computer science. The design of architecture for
storing such data requires a set of tools and frameworks such as SQL database
management systems and service-oriented frameworks. The proposed solution is
based on a multi-layer architecture, which allows to replace any component
without recompilation of other components. The approach contains five
components, i.e. Model, Base Engine, Concrete Engine, CBIR service and
Presentation. They were based on two well-known design patterns: Dependency
Injection and Inverse of Control. For experimental purposes we implemented the
SURF local interest point detector as a feature extractor and -means
clustering as indexer. The presented architecture is intended for content-based
retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial
Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan
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