13,619 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
Using Apache Lucene to Search Vector of Locally Aggregated Descriptors
Surrogate Text Representation (STR) is a profitable solution to efficient
similarity search on metric space using conventional text search engines, such
as Apache Lucene. This technique is based on comparing the permutations of some
reference objects in place of the original metric distance. However, the
Achilles heel of STR approach is the need to reorder the result set of the
search according to the metric distance. This forces to use a support database
to store the original objects, which requires efficient random I/O on a fast
secondary memory (such as flash-based storages). In this paper, we propose to
extend the Surrogate Text Representation to specifically address a class of
visual metric objects known as Vector of Locally Aggregated Descriptors (VLAD).
This approach is based on representing the individual sub-vectors forming the
VLAD vector with the STR, providing a finer representation of the vector and
enabling us to get rid of the reordering phase. The experiments on a publicly
available dataset show that the extended STR outperforms the baseline STR
achieving satisfactory performance near to the one obtained with the original
VLAD vectors.Comment: In Proceedings of the 11th Joint Conference on Computer Vision,
Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) -
Volume 4: VISAPP, p. 383-39
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