As online image sharing services are becoming popular, the importance of correctly annotated tags is being emphasized
for precise search and retrieval. Tags created by user along with user-generated contents (UGC) are often ambiguous due
to the fact that some tags are highly subjective and visually unrelated to the image. They cause unwanted results to users
when image search engines rely on tags. In this paper, we propose a method of measuring tag confidence so that one can
differentiate confidence tags from noisy tags. The proposed tag confidence is measured from visual semantics of the
image. To verify the usefulness of the proposed method, experiments were performed with UGC database from social
network sites. Experimental results showed that the image retrieval performance with confidence tags was increased.
KEYWORDS: Multimedia, Mobile devices, Internet, Laminated object manufacturing, Telecommunications, Internet technology, Statistical modeling, Statistical analysis, Personal digital assistants, Zoom lenses
As the Internet and multimedia technology is becoming advanced, the number of digital multimedia contents is also
becoming abundant in learning area. In order to facilitate the access of digital knowledge and to meet the need of a
lifelong learning, e-learning could be the helpful alternative way to the conventional learning paradigms. E-learning is
known as a unifying term to express online, web-based and technology-delivered learning. Mobile-learning (m-learning)
is defined as e-learning through mobile devices using wireless transmission. In a survey, more than half of the people
remarked that the re-consumption was one of the convenient features in e-learning. However, it is not easy to find user's
preferred segmentation from a full version of lengthy e-learning content. Especially in m-learning, a content-summarization
method is strongly required because mobile devices are limited to low processing power and battery
capacity. In this paper, we propose a new user preference model for re-consumption to construct personalized
summarization for re-consumption. The user preference for re-consumption is modeled based on user actions with
statistical model. Based on the user preference model for re-consumption with personalized user actions, our method
discriminates preferred parts over the entire content. Experimental results demonstrated successful personalized
summarization.
In this paper, we propose a new promising photo album application format, which enables augmented use of digital
home photos over a wide range of mobile devices and semantic photo consumption as minimizing user's manual tasks.
The photo album application format packages photo collection and associated metadata based on MPEG-4 file format.
The schema of the album metadata is designed in two levels: collection- and item-level descriptions. The collection-level
description is metadata related to group of photos, each of which has item-level description that contains its
detailed information. To demonstrate the use of the proposed album format on mobile devices, a photo album system
was also developed, which could realize semantic photo consumption in sense of situation, category, and person.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.