123,767 research outputs found
Review on Service Recommendation System using Social User?s Rating Behaviors
The research communities of information retrieval, machine learning and data mining are recently started to paying attention towards Service recommendation systems. Traditional service recommendation algorithms are often based on batch machine learning methods which are having certain critical limitations, e.g., mostly systems are so costly also new user needs to pay the certain cost for new login, can?t capture the changes of user preferences over time. So that to overcome from that problem it is important to make service recommendation system more flexible for real world online applications where data arrives sequentially and user preferences may change randomly and dynamically. The proposed system present a new framework of online social recommendation on the basis of online graph regularized user preference learning (OGRPL), which incorporates both collaborative user-services relationship as well as service content features into an unified preference learning process. Also provide aggregated services in only one application (social networking) which increases user?s interest towards the services. Proposed system also provides security about subscribed services as well as documents/photos on online social network application. Proposed system utilizes services like Active Life, Beauty & Spas, Home Services, Hotels & Travel, Pets, Restaurants and Shopping
Interactive retrieval of video using pre-computed shot-shot similarities
A probabilistic framework for content-based interactive video retrieval is described. The developed indexing of video fragments originates from the probability of the user's positive judgment about key-frames of video shots. Initial estimates of the probabilities are obtained from low-level feature representation. Only statistically significant estimates are picked out, the rest are replaced by an appropriate constant allowing efficient access at search time without loss of search quality and leading to improvement in most experiments. With time, these probability estimates are updated from the relevance judgment of users performing searches, resulting in further substantial increases in mean average precision
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
Sparse Transfer Learning for Interactive Video Search Reranking
Visual reranking is effective to improve the performance of the text-based
video search. However, existing reranking algorithms can only achieve limited
improvement because of the well-known semantic gap between low level visual
features and high level semantic concepts. In this paper, we adopt interactive
video search reranking to bridge the semantic gap by introducing user's
labeling effort. We propose a novel dimension reduction tool, termed sparse
transfer learning (STL), to effectively and efficiently encode user's labeling
information. STL is particularly designed for interactive video search
reranking. Technically, it a) considers the pair-wise discriminative
information to maximally separate labeled query relevant samples from labeled
query irrelevant ones, b) achieves a sparse representation for the subspace to
encodes user's intention by applying the elastic net penalty, and c) propagates
user's labeling information from labeled samples to unlabeled samples by using
the data distribution knowledge. We conducted extensive experiments on the
TRECVID 2005, 2006 and 2007 benchmark datasets and compared STL with popular
dimension reduction algorithms. We report superior performance by using the
proposed STL based interactive video search reranking.Comment: 17 page
A Machine Learning Based Analytical Framework for Semantic Annotation Requirements
The Semantic Web is an extension of the current web in which information is
given well-defined meaning. The perspective of Semantic Web is to promote the
quality and intelligence of the current web by changing its contents into
machine understandable form. Therefore, semantic level information is one of
the cornerstones of the Semantic Web. The process of adding semantic metadata
to web resources is called Semantic Annotation. There are many obstacles
against the Semantic Annotation, such as multilinguality, scalability, and
issues which are related to diversity and inconsistency in content of different
web pages. Due to the wide range of domains and the dynamic environments that
the Semantic Annotation systems must be performed on, the problem of automating
annotation process is one of the significant challenges in this domain. To
overcome this problem, different machine learning approaches such as supervised
learning, unsupervised learning and more recent ones like, semi-supervised
learning and active learning have been utilized. In this paper we present an
inclusive layered classification of Semantic Annotation challenges and discuss
the most important issues in this field. Also, we review and analyze machine
learning applications for solving semantic annotation problems. For this goal,
the article tries to closely study and categorize related researches for better
understanding and to reach a framework that can map machine learning techniques
into the Semantic Annotation challenges and requirements
Ontology technology for the development and deployment of learning technology systems - a survey
The World-Wide Web is undergoing dramatic changes at the moment. The Semantic Web is an initiative to bring meaning to the Web. The Semantic Web is based on ontology
technology â a knowledge representation framework â at its core. We illustrate the importance of this evolutionary development. We survey five scenarios demonstrating different forms of applications of ontology technologies in the development and deployment of learning technology
systems. Ontology technologies are highly useful to organise, personalise, and publish learning content and to discover, generate, and compose learning objects
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
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