14,689 research outputs found
Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques
Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to
be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning
methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories.
We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that
proposes a new form of interaction between users and digital libraries, where the latter are adapted to users
and their surroundings
Efficient video indexing for monitoring disease activity and progression in the upper gastrointestinal tract
Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. While the endoscopy video contains a
wealth of information, tools to capture this information for the purpose of
clinical reporting are rather poor. In date, endoscopists do not have any
access to tools that enable them to browse the video data in an efficient and
user friendly manner. Fast and reliable video retrieval methods could for
example, allow them to review data from previous exams and therefore improve
their ability to monitor disease progression. Deep learning provides new
avenues of compressing and indexing video in an extremely efficient manner. In
this study, we propose to use an autoencoder for efficient video compression
and fast retrieval of video images. To boost the accuracy of video image
retrieval and to address data variability like multi-modality and view-point
changes, we propose the integration of a Siamese network. We demonstrate that
our approach is competitive in retrieving images from 3 large scale videos of 3
different patients obtained against the query samples of their previous
diagnosis. Quantitative validation shows that the combined approach yield an
overall improvement of 5% and 8% over classical and variational autoencoders,
respectively.Comment: Accepted at IEEE International Symposium on Biomedical Imaging
(ISBI), 201
Intelligent Image Retrieval Techniques: A Survey
AbstractIn the current era of digital communication, the use of digital images has increased for expressing, sharing and interpreting information. While working with digital images, quite often it is necessary to search for a specific image for a particular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of images but it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same content-based searching task becomes extremely complex when the number of images is in the millions. To deal with the situation, some intelligent way of content-based searching is required to fulfill the searching request with right visual contents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficient and robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques
Autoencoder-based Image Recommendation for Lung Cancer Characterization
Neste projeto, temos como objetivo desenvolver um sistema de IA que recomende um conjunto de casos relativos (passados) para orientar a tomada de decisão do médico.
Objetivo: A ambição é desenvolver um modelo de aprendizado baseado em IA para caracterização de câncer de pulmão, a fim de auxiliar na rotina clínica. Considerando a complexidade dos fenômenos biológicos que ocorrem durante o desenvolvimento do câncer, as relações entre eles e as manifestações visuais capturadas pela tomografia computadorizada (CT) têm sido exploradas nos últimos anos. No entanto, devido à falta de robustez dos métodos atuais de aprendizado profundo, essas correlações são frequentemente consideradas espúrias e se perdem quando confrontadas com dados coletados a partir de distribuições alteradas: diferentes instituições, características demográficas ou até mesmo estágios de desenvolvimento do câncer.In this project, we aim to develop an AI system that recommends a set of relative (past) cases to guide the decision-making of the clinician.
Objective: The ambition is to develop an AI-based learning model for lung cancer characterization in order to assist in clinical routine. Considering the complexity of the biological phenomenat hat occur during cancer development, relationships between these and visual manifestations captured by CT have been explored in recent years; however, given the lack of robustness of current deep learning methods, these correlations are often found spurious and get lost when facing data collected from shifted distributions: different institutions, demographics or even stages of cancer development
Enhancing Automatic Annotation for Optimal Image Retrieval
Image search and retrieval based on content is very cumbersome task particularly when the image database is large. The accuracy of the retrieval as well as the processing speed are two important measures used for assessing and comparing the effectiveness of various systems.
Text retrieval is more mature and advanced than image content retrieval. In this dissertation, the focus is on converting image content into text tags that can be easily searched using standard search engines where the size and speed issues of the database have been already dealt with.
Therefore, image tagging becomes an essential tool for image retrieval from large image databases. Automation of image tagging has received considerable attention by many researchers in recent years. The optimal goal of image description is to automatically annotate images with tags that semantically represent the image content. The speed and accuracy of Image retrieval from large databases are few of the important domains that can benefit from automatic tagging.
In this work, several state of the art image classification and image tagging techniques are reviewed. We propose a new self-learning multilayered tagging framework that can address the limitations of current approaches and provide mutual accuracy improvement between the recognition layer and the annotation layer. Our results indicate that the proposed framework can improve the overall accuracy of information retrieval in a variety of image databases
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Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval.
Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches
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