61,806 research outputs found
A Dilated Inception Network for Visual Saliency Prediction
Recently, with the advent of deep convolutional neural networks (DCNN), the
improvements in visual saliency prediction research are impressive. One
possible direction to approach the next improvement is to fully characterize
the multi-scale saliency-influential factors with a computationally-friendly
module in DCNN architectures. In this work, we proposed an end-to-end dilated
inception network (DINet) for visual saliency prediction. It captures
multi-scale contextual features effectively with very limited extra parameters.
Instead of utilizing parallel standard convolutions with different kernel sizes
as the existing inception module, our proposed dilated inception module (DIM)
uses parallel dilated convolutions with different dilation rates which can
significantly reduce the computation load while enriching the diversity of
receptive fields in feature maps. Moreover, the performance of our saliency
model is further improved by using a set of linear normalization-based
probability distribution distance metrics as loss functions. As such, we can
formulate saliency prediction as a probability distribution prediction task for
global saliency inference instead of a typical pixel-wise regression problem.
Experimental results on several challenging saliency benchmark datasets
demonstrate that our DINet with proposed loss functions can achieve
state-of-the-art performance with shorter inference time.Comment: Accepted by IEEE Transactions on Multimedia. The source codes are
available at https://github.com/ysyscool/DINe
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
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
In this paper, we present an automated approach for segmenting multiple
sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our
method is based on a deep end-to-end 2D convolutional neural network (CNN) for
slice-based segmentation of 3D volumetric data. The proposed CNN includes a
multi-branch downsampling path, which enables the network to encode information
from multiple modalities separately. Multi-scale feature fusion blocks are
proposed to combine feature maps from different modalities at different stages
of the network. Then, multi-scale feature upsampling blocks are introduced to
upsize combined feature maps to leverage information from lesion shape and
location. We trained and tested the proposed model using orthogonal plane
orientations of each 3D modality to exploit the contextual information in all
directions. The proposed pipeline is evaluated on two different datasets: a
private dataset including 37 MS patients and a publicly available dataset known
as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset,
consisting of 14 MS patients. Considering the ISBI challenge, at the time of
submission, our method was amongst the top performing solutions. On the private
dataset, using the same array of performance metrics as in the ISBI challenge,
the proposed approach shows high improvements in MS lesion segmentation
compared with other publicly available tools.Comment: This paper has been accepted for publication in NeuroImag
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