6,140 research outputs found
EGO: a personalised multimedia management tool
The problems of Content-Based Image Retrieval (CBIR) sys- tems can be attributed to the semantic gap between the low-level data representation and the high-level concepts the user associates with images, on the one hand, and the time-varying and often vague nature of the underlying information need, on the other. These problems can be addressed by improving the interaction between the user and the system. In this paper, we sketch the development of CBIR interfaces, and introduce our view on how to solve some of the problems of the studied interfaces. To address the semantic gap and long-term multifaceted information needs, we propose a "retrieval in context" system. EGO is a tool for the management of image collections, supporting the user through personalisation and adaptation. We will describe how it learns from the user's personal organisation, allowing it to recommend relevant images to the user. The recommendation algorithm is detailed, which is based on relevance feedback techniques
Exploring EEG for Object Detection and Retrieval
This paper explores the potential for using Brain Computer Interfaces (BCI)
as a relevance feedback mechanism in content-based image retrieval. We
investigate if it is possible to capture useful EEG signals to detect if
relevant objects are present in a dataset of realistic and complex images. We
perform several experiments using a rapid serial visual presentation (RSVP) of
images at different rates (5Hz and 10Hz) on 8 users with different degrees of
familiarization with BCI and the dataset. We then use the feedback from the BCI
and mouse-based interfaces to retrieve localized objects in a subset of TRECVid
images. We show that it is indeed possible to detect such objects in complex
images and, also, that users with previous knowledge on the dataset or
experience with the RSVP outperform others. When the users have limited time to
annotate the images (100 seconds in our experiments) both interfaces are
comparable in performance. Comparing our best users in a retrieval task, we
found that EEG-based relevance feedback outperforms mouse-based feedback. The
realistic and complex image dataset differentiates our work from previous
studies on EEG for image retrieval.Comment: This preprint is the full version of a short paper accepted in the
ACM International Conference on Multimedia Retrieval (ICMR) 2015 (Shanghai,
China
Affective Music Information Retrieval
Much of the appeal of music lies in its power to convey emotions/moods and to
evoke them in listeners. In consequence, the past decade witnessed a growing
interest in modeling emotions from musical signals in the music information
retrieval (MIR) community. In this article, we present a novel generative
approach to music emotion modeling, with a specific focus on the
valence-arousal (VA) dimension model of emotion. The presented generative
model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the
subjectivity of emotion perception by the use of probability distributions.
Specifically, it learns from the emotion annotations of multiple subjects a
Gaussian mixture model in the VA space with prior constraints on the
corresponding acoustic features of the training music pieces. Such a
computational framework is technically sound, capable of learning in an online
fashion, and thus applicable to a variety of applications, including
user-independent (general) and user-dependent (personalized) emotion
recognition and emotion-based music retrieval. We report evaluations of the
aforementioned applications of AEG on a larger-scale emotion-annotated corpora,
AMG1608, to demonstrate the effectiveness of AEG and to showcase how
evaluations are conducted for research on emotion-based MIR. Directions of
future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio
Aggregated Deep Local Features for Remote Sensing Image Retrieval
Remote Sensing Image Retrieval remains a challenging topic due to the special
nature of Remote Sensing Imagery. Such images contain various different
semantic objects, which clearly complicates the retrieval task. In this paper,
we present an image retrieval pipeline that uses attentive, local convolutional
features and aggregates them using the Vector of Locally Aggregated Descriptors
(VLAD) to produce a global descriptor. We study various system parameters such
as the multiplicative and additive attention mechanisms and descriptor
dimensionality. We propose a query expansion method that requires no external
inputs. Experiments demonstrate that even without training, the local
convolutional features and global representation outperform other systems.
After system tuning, we can achieve state-of-the-art or competitive results.
Furthermore, we observe that our query expansion method increases overall
system performance by about 3%, using only the top-three retrieved images.
Finally, we show how dimensionality reduction produces compact descriptors with
increased retrieval performance and fast retrieval computation times, e.g. 50%
faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal
contributio
A survey on the use of relevance feedback for information access systems
Users of online search engines often find it difficult to express their need for information in the form of a query. However, if the user can identify examples of the kind of documents they require then they can employ a technique known as relevance feedback. Relevance feedback covers a range of techniques intended to improve a user's query and facilitate retrieval of information relevant to a user's information need. In this paper we survey relevance feedback techniques. We study both automatic techniques, in which the system modifies the user's query, and interactive techniques, in which the user has control over query modification. We also consider specific interfaces to relevance feedback systems and characteristics of searchers that can affect the use and success of relevance feedback systems
MIRACLE Retrieval Experiments with East Asian Languages
This paper describes the participation of MIRACLE in NTCIR 2005 CLIR task. Although our group has a strong background and long expertise in Computational Linguistics and Information Retrieval applied to European languages and using Latin and Cyrillic alphabets, this was our first attempt on East Asian languages. Our main goal was to study the particularities and distinctive characteristics of Japanese, Chinese and Korean, specially focusing on the similarities and differences with European languages, and carry out research on CLIR tasks which include those languages. The basic idea behind our participation in NTCIR is to test if the same familiar linguisticbased techniques may also applicable to East Asian languages, and study the necessary adaptations
Integrating Medical Ontology and Pseudo Relevance Feedback For Medical Document Retrieval
The purpose of this thesis is to undertake and improve the accuracy of locating the relevant documents from a large amount of Electronic Medical Data (EMD). The unique goal of this research is to propose a new idea for using medical ontology to find an easy and more reliable approach for patients to have a better understanding of their diseases and also help doctors to find and further improve the possible methods of diagnosis and treatments. The empirical studies were based on the dataset provided by CLEF focused on health care data. In this research, I have used Information Retrieval to find and obtain relevant information within the large amount of data sets provided by CLEF. I then used ranking functionality on the Terrier platform to calculate and evaluate the matching documents in the collection of data sets. BM25 was used as the base normalization method to retrieve the results and Pseudo Relevance Feedback weighting model to retrieve the information regarding patients health history and medical records in order to find more accurate results. I then used Unified Medical Language System to develop indexing of the queries while searching on the Internet and looking for health related documents. UMLS software was actually used to link the computer system with the health and biomedical terms and vocabularies into classify tools; it works as a dictionary for the patients by translating the medical terms. Later I would like to work on using medical ontology to create a relationship between the documents regarding the medical data and my retrieved results
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