34 research outputs found
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
Direction Selective Contour Detection for Salient Objects
The active contour model is a widely used technique
for automatic object contour extraction. Existing methods based
on this model can perform with high accuracy even in case of
complex contours, but challenging issues remain, like the need
for precise contour initialization for high curvature boundary
segments or the handling of cluttered backgrounds. To deal
with such issues, this paper presents a salient object extraction
method, the first step of which is the introduction of an improved
edge map that incorporates edge direction as a feature. The
direction information in the small neighborhoods of image feature
points are extracted, and the images’ prominent orientations
are defined for direction-selective edge extraction. Using such
improved edge information, we provide a highly accurate shape
contour representation, which we also combine with texture
features. The principle of the paper is to interpret an object as
the fusion of its components: its extracted contour and its inner
texture. Our goal in fusing textural and structural information is
twofold: it is applied for automatic contour initialization, and it is
also used to establish an improved external force field. This fusion
then produces highly accurate salient object extractions. We
performed extensive evaluations which confirm that the presented
object extraction method outperforms parametric active contour
models and achieves higher efficiency than the majority of the
evaluated automatic saliency methods
Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement.
Visual attention is a kind of fundamental cognitive capability that allows human beings to focus on the region of interests (ROIs) under complex natural environments. What kind of ROIs that we pay attention to mainly depends on two distinct types of attentional mechanisms. The bottom-up mechanism can guide our detection of the salient objects and regions by externally driven factors, i.e. color and location, whilst the top-down mechanism controls our biasing attention based on prior knowledge and cognitive strategies being provided by visual cortex. However, how to practically use and fuse both attentional mechanisms for salient object detection has not been sufficiently explored. To the end, we propose in this paper an integrated framework consisting of bottom-up and top-down attention mechanisms that enable attention to be computed at the level of salient objects and/or regions. Within our framework, the model of a bottom-up mechanism is guided by the gestalt-laws of perception. We interpreted gestalt-laws of homogeneity, similarity, proximity and figure and ground in link with color, spatial contrast at the level of regions and objects to produce feature contrast map. The model of top-down mechanism aims to use a formal computational model to describe the background connectivity of the attention and produce the priority map. Integrating both mechanisms and applying to salient object detection, our results have demonstrated that the proposed method consistently outperforms a number of existing unsupervised approaches on five challenging and complicated datasets in terms of higher precision and recall rates, AP (average precision) and AUC (area under curve) values