104 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
Advanced Visual Computing for Image Saliency Detection
Saliency detection is a category of computer vision algorithms that aims to filter out the most salient object in a given image. Existing saliency detection methods can generally be categorized as bottom-up methods and top-down methods, and the prevalent deep neural network (DNN) has begun to show its applications in saliency detection in recent years. However, the challenges in existing methods, such as problematic pre-assumption, inefficient feature integration and absence of high-level feature learning, prevent them from superior performances. In this thesis, to address the limitations above, we have proposed multiple novel models with favorable performances. Specifically, we first systematically reviewed the developments of saliency detection and its related works, and then proposed four new methods, with two based on low-level image features, and two based on DNNs. The regularized random walks ranking method (RR) and its reversion-correction-improved version (RCRR) are based on conventional low-level image features, which exhibit higher accuracy and robustness in extracting the image boundary based foreground / background queries; while the background search and foreground estimation (BSFE) and dense and sparse labeling (DSL) methods are based on DNNs, which have shown their dominant advantages in high-level image feature extraction, as well as the combined strength of multi-dimensional features. Each of the proposed methods is evaluated by extensive experiments, and all of them behave favorably against the state-of-the-art, especially the DSL method, which achieves remarkably higher performance against sixteen state-of-the-art methods (including ten conventional methods and six learning based methods) on six well-recognized public datasets. The successes of our proposed methods reveal more potential and meaningful applications of saliency detection in real-life computer vision tasks
Salient Object Detection Techniques in Computer Vision-A Survey.
Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end
Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection
Top-down saliency models produce a probability map that peaks at target
locations specified by a task/goal such as object detection. They are usually
trained in a fully supervised setting involving pixel-level annotations of
objects. We propose a weakly supervised top-down saliency framework using only
binary labels that indicate the presence/absence of an object in an image.
First, the probabilistic contribution of each image region to the confidence of
a CNN-based image classifier is computed through a backtracking strategy to
produce top-down saliency. From a set of saliency maps of an image produced by
fast bottom-up saliency approaches, we select the best saliency map suitable
for the top-down task. The selected bottom-up saliency map is combined with the
top-down saliency map. Features having high combined saliency are used to train
a linear SVM classifier to estimate feature saliency. This is integrated with
combined saliency and further refined through a multi-scale
superpixel-averaging of saliency map. We evaluate the performance of the
proposed weakly supervised topdown saliency and achieve comparable performance
with fully supervised approaches. Experiments are carried out on seven
challenging datasets and quantitative results are compared with 40 closely
related approaches across 4 different applications.Comment: 14 pages, 7 figure
- …