864 research outputs found
Robust Brain MRI Image Classification with SIBOW-SVM
The majority of primary Central Nervous System (CNS) tumors in the brain are
among the most aggressive diseases affecting humans. Early detection of brain
tumor types, whether benign or malignant, glial or non-glial, is critical for
cancer prevention and treatment, ultimately improving human life expectancy.
Magnetic Resonance Imaging (MRI) stands as the most effective technique to
detect brain tumors by generating comprehensive brain images through scans.
However, human examination can be error-prone and inefficient due to the
complexity, size, and location variability of brain tumors. Recently, automated
classification techniques using machine learning (ML) methods, such as
Convolutional Neural Network (CNN), have demonstrated significantly higher
accuracy than manual screening, while maintaining low computational costs.
Nonetheless, deep learning-based image classification methods, including CNN,
face challenges in estimating class probabilities without proper model
calibration. In this paper, we propose a novel brain tumor image classification
method, called SIBOW-SVM, which integrates the Bag-of-Features (BoF) model with
SIFT feature extraction and weighted Support Vector Machines (wSVMs). This new
approach effectively captures hidden image features, enabling the
differentiation of various tumor types and accurate label predictions.
Additionally, the SIBOW-SVM is able to estimate the probabilities of images
belonging to each class, thereby providing high-confidence classification
decisions. We have also developed scalable and parallelable algorithms to
facilitate the practical implementation of SIBOW-SVM for massive images. As a
benchmark, we apply the SIBOW-SVM to a public data set of brain tumor MRI
images containing four classes: glioma, meningioma, pituitary, and normal. Our
results show that the new method outperforms state-of-the-art methods,
including CNN
Deep Convolutional Ranking for Multilabel Image Annotation
Multilabel image annotation is one of the most important challenges in
computer vision with many real-world applications. While existing work usually
use conventional visual features for multilabel annotation, features based on
Deep Neural Networks have shown potential to significantly boost performance.
In this work, we propose to leverage the advantage of such features and analyze
key components that lead to better performances. Specifically, we show that a
significant performance gain could be obtained by combining convolutional
architectures with approximate top- ranking objectives, as thye naturally
fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset
outperforms the conventional visual features by about 10%, obtaining the best
reported performance in the literature
Efficient Match Pair Retrieval for Large-scale UAV Images via Graph Indexed Global Descriptor
SfM (Structure from Motion) has been extensively used for UAV (Unmanned
Aerial Vehicle) image orientation. Its efficiency is directly influenced by
feature matching. Although image retrieval has been extensively used for match
pair selection, high computational costs are consumed due to a large number of
local features and the large size of the used codebook. Thus, this paper
proposes an efficient match pair retrieval method and implements an integrated
workflow for parallel SfM reconstruction. First, an individual codebook is
trained online by considering the redundancy of UAV images and local features,
which avoids the ambiguity of training codebooks from other datasets. Second,
local features of each image are aggregated into a single high-dimension global
descriptor through the VLAD (Vector of Locally Aggregated Descriptors)
aggregation by using the trained codebook, which remarkably reduces the number
of features and the burden of nearest neighbor searching in image indexing.
Third, the global descriptors are indexed via the HNSW (Hierarchical Navigable
Small World) based graph structure for the nearest neighbor searching. Match
pairs are then retrieved by using an adaptive threshold selection strategy and
utilized to create a view graph for divide-and-conquer based parallel SfM
reconstruction. Finally, the performance of the proposed solution has been
verified using three large-scale UAV datasets. The test results demonstrate
that the proposed solution accelerates match pair retrieval with a speedup
ratio ranging from 36 to 108 and improves the efficiency of SfM reconstruction
with competitive accuracy in both relative and absolute orientation
Experimenting Liver Fibrosis Diagnostic by Two Photon Excitation Microscopy and Bag-of-Features Image Classification
The accurate staging of liver fibrosis is of paramount importance to determine the state of disease progression, therapy responses, and to optimize disease treatment strategies. Non-linear optical microscopy techniques such as two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG) can image the endogenous signals of tissue structures and can be used for fibrosis assessment on non-stained tissue samples. While image analysis of collagen in SHG images was consistently addressed until now, cellular and tissue information included in TPEF images, such as inflammatory and hepatic cell damage, equally important as collagen deposition imaged by SHG, remain poorly exploited to date. We address this situation by experimenting liver fibrosis quantification and scoring using a combined approach based on TPEF liver surface imaging on a Thioacetamide-induced rat model and a gradient based Bag-of-Features (BoF) image classification strategy. We report the assessed performance results and discuss the influence of specific BoF parameters to the performance of the fibrosis scoring framework.Romania. Executive Agency for Higher Education, Research, Development and Innovation Funding (research grant PN-II-PT-PCCA-2011-3.2-1162)Rectors' Conference of the Swiss Universities (SCIEX NMS-CH research fellowship nr. 12.135)Singapore. Agency for Science, Technology and Research (R-185-000-182-592)Singapore. Biomedical Research CouncilInstitute of Bioengineering and Nanotechnology (Singapore)Singapore-MIT Alliance (Computational and Systems Biology Flagship Project funding (C-382-641-001-091))Singapore-MIT Alliance for Research and Technology (SMART BioSyM and Mechanobiology Institute of Singapore (R-714-001-003-271)
Action Recognition in Videos: from Motion Capture Labs to the Web
This paper presents a survey of human action recognition approaches based on
visual data recorded from a single video camera. We propose an organizing
framework which puts in evidence the evolution of the area, with techniques
moving from heavily constrained motion capture scenarios towards more
challenging, realistic, "in the wild" videos. The proposed organization is
based on the representation used as input for the recognition task, emphasizing
the hypothesis assumed and thus, the constraints imposed on the type of video
that each technique is able to address. Expliciting the hypothesis and
constraints makes the framework particularly useful to select a method, given
an application. Another advantage of the proposed organization is that it
allows categorizing newest approaches seamlessly with traditional ones, while
providing an insightful perspective of the evolution of the action recognition
task up to now. That perspective is the basis for the discussion in the end of
the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4
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