10,784 research outputs found
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
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
The relationship between IR and multimedia databases
Modern extensible database systems support multimedia data through ADTs. However, because of the problems with multimedia query formulation, this support is not sufficient.\ud
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Multimedia querying requires an iterative search process involving many different representations of the objects in the database. The support that is needed is very similar to the processes in information retrieval.\ud
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Based on this observation, we develop the miRRor architecture for multimedia query processing. We design a layered framework based on information retrieval techniques, to provide a usable query interface to the multimedia database.\ud
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First, we introduce a concept layer to enable reasoning over low-level concepts in the database.\ud
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Second, we add an evidential reasoning layer as an intermediate between the user and the concept layer.\ud
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Third, we add the functionality to process the users' relevance feedback.\ud
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We then adapt the inference network model from text retrieval to an evidential reasoning model for multimedia query processing.\ud
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We conclude with an outline for implementation of miRRor on top of the Monet extensible database system
A Density-Based Approach to the Retrieval of Top-K Spatial Textual Clusters
Keyword-based web queries with local intent retrieve web content that is
relevant to supplied keywords and that represent points of interest that are
near the query location. Two broad categories of such queries exist. The first
encompasses queries that retrieve single spatial web objects that each satisfy
the query arguments. Most proposals belong to this category. The second
category, to which this paper's proposal belongs, encompasses queries that
support exploratory user behavior and retrieve sets of objects that represent
regions of space that may be of interest to the user. Specifically, the paper
proposes a new type of query, namely the top-k spatial textual clusters (k-STC)
query that returns the top-k clusters that (i) are located the closest to a
given query location, (ii) contain the most relevant objects with regard to
given query keywords, and (iii) have an object density that exceeds a given
threshold. To compute this query, we propose a basic algorithm that relies on
on-line density-based clustering and exploits an early stop condition. To
improve the response time, we design an advanced approach that includes three
techniques: (i) an object skipping rule, (ii) spatially gridded posting lists,
and (iii) a fast range query algorithm. An empirical study on real data
demonstrates that the paper's proposals offer scalability and are capable of
excellent performance
Recent Developments in Cultural Heritage Image Databases: Directions for User-Centered Design
published or submitted for publicatio
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