44,984 research outputs found
Automated annotation of landmark images using community contributed datasets and web resources
A novel solution to the challenge of automatic image annotation is described. Given an image with GPS data of its location of capture, our system returns a semantically-rich annotation comprising tags which both identify the landmark in the image, and provide an interesting fact about it, e.g. "A view of the Eiffel Tower, which was built in 1889 for an international exhibition in Paris". This exploits visual and textual web mining in combination with content-based image
analysis and natural language processing. In the first stage, an input image is matched to a set of community contributed images (with keyword tags) on the basis of its GPS information and image classification techniques. The depicted landmark is inferred from the keyword tags for the matched set. The system then takes advantage of the information written about landmarks available on the web at large to extract a fact about the landmark in the image. We report component evaluation results from an implementation of our solution on a mobile device. Image localisation and matching oers 93.6% classication accuracy; the selection of appropriate tags for use in annotation performs well (F1M of
0.59), and it subsequently automatically identies a correct toponym for use in captioning and fact extraction in 69.0% of the tested cases; finally the fact extraction returns an interesting caption in 78% of cases
Ensemble classification and signal image processing for genus Gyrodactylus (Monogenea)
This thesis presents an investigation into Gyrodactylus species recognition, making use of machine learning classification and feature selection techniques, and explores image feature extraction to demonstrate proof of concept for an envisaged rapid, consistent and secure initial identification of pathogens by field workers and non-expert users. The design of the proposed cognitively inspired framework is able to provide confident discrimination recognition from its non-pathogenic congeners, which is sought in order to assist diagnostics
during periods of a suspected outbreak. Accurate identification of pathogens is a key to their control in an aquaculture context and the monogenean worm genus Gyrodactylus provides an ideal test-bed for the selected techniques. In the proposed algorithm, the concept of classification using a single model is extended to include more than one model. In classifying multiple species of Gyrodactylus, experiments using 557 specimens of nine different species, two classifiers and three feature sets were performed. To combine these models, an ensemble based majority voting approach has been adopted. Experimental results with a database of Gyrodactylus species show the superior performance of the ensemble system. Comparison
with single classification approaches indicates that the proposed framework produces a marked improvement in classification performance. The second contribution of this thesis is the exploration of image processing techniques. Active Shape Model (ASM) and Complex Network methods are applied to images of the attachment hooks of several species of Gyrodactylus to classify each species according to their true species type. ASM is used to provide landmark points to segment the contour of the image, while the Complex Network model is used to extract the information from the contour of an image. The current system
aims to confidently classify species, which is notifiable pathogen of Atlantic salmon, to their true class with high degree of accuracy. Finally, some concluding remarks are made along with proposal for future work
Visual and geographical data fusion to classify landmarks in geo-tagged images
High level semantic image recognition and classification is a challenging task and currently is a very active research domain. Computers struggle with the high level task of identifying objects and scenes within digital images accurately in unconstrained environments. In this paper, we present experiments that aim to overcome the limitations of computer vision algorithms by combining them with novel contextual based features to describe geo-tagged imagery. We adopt a machine learning based algorithm with the aim of classifying classes of geographical landmarks within digital images. We use community contributed image sets downloaded from Flickr and provide a thorough investigation, the results of which are presented in an evaluation section
Diffusion Model as Representation Learner
Diffusion Probabilistic Models (DPMs) have recently demonstrated impressive
results on various generative tasks.Despite its promises, the learned
representations of pre-trained DPMs, however, have not been fully understood.
In this paper, we conduct an in-depth investigation of the representation power
of DPMs, and propose a novel knowledge transfer method that leverages the
knowledge acquired by generative DPMs for recognition tasks. Our study begins
by examining the feature space of DPMs, revealing that DPMs are inherently
denoising autoencoders that balance the representation learning with
regularizing model capacity. To this end, we introduce a novel knowledge
transfer paradigm named RepFusion. Our paradigm extracts representations at
different time steps from off-the-shelf DPMs and dynamically employs them as
supervision for student networks, in which the optimal time is determined
through reinforcement learning. We evaluate our approach on several image
classification, semantic segmentation, and landmark detection benchmarks, and
demonstrate that it outperforms state-of-the-art methods. Our results uncover
the potential of DPMs as a powerful tool for representation learning and
provide insights into the usefulness of generative models beyond sample
generation. The code is available at
\url{https://github.com/Adamdad/Repfusion}.Comment: Accepted by ICCV 202
Deep learning cardiac motion analysis for human survival prediction
Motion analysis is used in computer vision to understand the behaviour of
moving objects in sequences of images. Optimising the interpretation of dynamic
biological systems requires accurate and precise motion tracking as well as
efficient representations of high-dimensional motion trajectories so that these
can be used for prediction tasks. Here we use image sequences of the heart,
acquired using cardiac magnetic resonance imaging, to create time-resolved
three-dimensional segmentations using a fully convolutional network trained on
anatomical shape priors. This dense motion model formed the input to a
supervised denoising autoencoder (4Dsurvival), which is a hybrid network
consisting of an autoencoder that learns a task-specific latent code
representation trained on observed outcome data, yielding a latent
representation optimised for survival prediction. To handle right-censored
survival outcomes, our network used a Cox partial likelihood loss function. In
a study of 302 patients the predictive accuracy (quantified by Harrell's
C-index) was significantly higher (p < .0001) for our model C=0.73 (95 CI:
0.68 - 0.78) than the human benchmark of C=0.59 (95 CI: 0.53 - 0.65). This
work demonstrates how a complex computer vision task using high-dimensional
medical image data can efficiently predict human survival
Persistent Evidence of Local Image Properties in Generic ConvNets
Supervised training of a convolutional network for object classification
should make explicit any information related to the class of objects and
disregard any auxiliary information associated with the capture of the image or
the variation within the object class. Does this happen in practice? Although
this seems to pertain to the very final layers in the network, if we look at
earlier layers we find that this is not the case. Surprisingly, strong spatial
information is implicit. This paper addresses this, in particular, exploiting
the image representation at the first fully connected layer, i.e. the global
image descriptor which has been recently shown to be most effective in a range
of visual recognition tasks. We empirically demonstrate evidences for the
finding in the contexts of four different tasks: 2d landmark detection, 2d
object keypoints prediction, estimation of the RGB values of input image, and
recovery of semantic label of each pixel. We base our investigation on a simple
framework with ridge rigression commonly across these tasks, and show results
which all support our insight. Such spatial information can be used for
computing correspondence of landmarks to a good accuracy, but should
potentially be useful for improving the training of the convolutional nets for
classification purposes
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