1,385 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
Hybrid image representation methods for automatic image annotation: a survey
In most automatic image annotation systems, images are represented with low level features using either global
methods or local methods. In global methods, the entire image is used as a unit. Local methods divide images into blocks where fixed-size sub-image blocks are adopted as sub-units; or into regions by using segmented regions as sub-units in images. In contrast to typical automatic image annotation methods that use either global or local features exclusively, several recent methods have considered incorporating the two kinds of information, and believe that the combination of the two levels of features is
beneficial in annotating images. In this paper, we provide a
survey on automatic image annotation techniques according to
one aspect: feature extraction, and, in order to complement
existing surveys in literature, we focus on the emerging image annotation methods: hybrid methods that combine both global and local features for image representation
Video browsing interfaces and applications: a review
We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video dataâwhich, if presented in its raw format, is rather unwieldy and costlyâhave become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other
Semantic Retrieval and Automatic Annotation: Linear Transformations, Correlation and Semantic Spaces
This paper proposes a new technique for auto-annotation and semantic retrieval based upon the idea of linearly mapping an image feature space to a keyword space. The new technique is compared to several related techniques, and a number of salient points about each of the techniques are discussed and contrasted. The paper also discusses how these techniques might actually scale to a real-world retrieval problem, and demonstrates this though a case study of a semantic retrieval technique being used on a real-world data-set (with a mix of annotated and unannotated images) from a picture library
An adaptive technique for content-based image retrieval
We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needsâa special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search
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