497 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
Deep Saliency with Encoded Low level Distance Map and High Level Features
Recent advances in saliency detection have utilized deep learning to obtain
high level features to detect salient regions in a scene. These advances have
demonstrated superior results over previous works that utilize hand-crafted low
level features for saliency detection. In this paper, we demonstrate that
hand-crafted features can provide complementary information to enhance
performance of saliency detection that utilizes only high level features. Our
method utilizes both high level and low level features for saliency detection
under a unified deep learning framework. The high level features are extracted
using the VGG-net, and the low level features are compared with other parts of
an image to form a low level distance map. The low level distance map is then
encoded using a convolutional neural network(CNN) with multiple 1X1
convolutional and ReLU layers. We concatenate the encoded low level distance
map and the high level features, and connect them to a fully connected neural
network classifier to evaluate the saliency of a query region. Our experiments
show that our method can further improve the performance of state-of-the-art
deep learning-based saliency detection methods.Comment: Accepted by IEEE Conference on Computer Vision and Pattern
Recognition(CVPR) 2016. Project page:
https://github.com/gylee1103/SaliencyEL
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