6,611 research outputs found
DISC: Deep Image Saliency Computing via Progressive Representation Learning
Salient object detection increasingly receives attention as an important
component or step in several pattern recognition and image processing tasks.
Although a variety of powerful saliency models have been intensively proposed,
they usually involve heavy feature (or model) engineering based on priors (or
assumptions) about the properties of objects and backgrounds. Inspired by the
effectiveness of recently developed feature learning, we provide a novel Deep
Image Saliency Computing (DISC) framework for fine-grained image saliency
computing. In particular, we model the image saliency from both the coarse- and
fine-level observations, and utilize the deep convolutional neural network
(CNN) to learn the saliency representation in a progressive manner.
Specifically, our saliency model is built upon two stacked CNNs. The first CNN
generates a coarse-level saliency map by taking the overall image as the input,
roughly identifying saliency regions in the global context. Furthermore, we
integrate superpixel-based local context information in the first CNN to refine
the coarse-level saliency map. Guided by the coarse saliency map, the second
CNN focuses on the local context to produce fine-grained and accurate saliency
map while preserving object details. For a testing image, the two CNNs
collaboratively conduct the saliency computing in one shot. Our DISC framework
is capable of uniformly highlighting the objects-of-interest from complex
background while preserving well object details. Extensive experiments on
several standard benchmarks suggest that DISC outperforms other
state-of-the-art methods and it also generalizes well across datasets without
additional training. The executable version of DISC is available online:
http://vision.sysu.edu.cn/projects/DISC.Comment: This manuscript is the accepted version for IEEE Transactions on
Neural Networks and Learning Systems (T-NNLS), 201
Visual Saliency Based on Multiscale Deep Features
Visual saliency is a fundamental problem in both cognitive and computational
sciences, including computer vision. In this CVPR 2015 paper, we discover that
a high-quality visual saliency model can be trained with multiscale features
extracted using a popular deep learning architecture, convolutional neural
networks (CNNs), which have had many successes in visual recognition tasks. For
learning such saliency models, we introduce a neural network architecture,
which has fully connected layers on top of CNNs responsible for extracting
features at three different scales. We then propose a refinement method to
enhance the spatial coherence of our saliency results. Finally, aggregating
multiple saliency maps computed for different levels of image segmentation can
further boost the performance, yielding saliency maps better than those
generated from a single segmentation. To promote further research and
evaluation of visual saliency models, we also construct a new large database of
4447 challenging images and their pixelwise saliency annotation. Experimental
results demonstrate that our proposed method is capable of achieving
state-of-the-art performance on all public benchmarks, improving the F-Measure
by 5.0% and 13.2% respectively on the MSRA-B dataset and our new dataset
(HKU-IS), and lowering the mean absolute error by 5.7% and 35.1% respectively
on these two datasets.Comment: To appear in CVPR 201
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