, Yichen Wei, Litian Tao, Chao Zhang, Jian Sun.
Salient Object Detection by Composition.
IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain, 2011.
Conventional saliency analysis methods measure the saliency of individual pixels. The resulting saliency map inevitably loses
information in the original image and finding salient objects in it is difficult. We propose
to detect salient objects by directly measuring the saliency of an image window in the original
image and adopt the well established sliding window based object detection paradigm. We present
a simple definition for window saliency, e.g. the cost of composing the window using the
remaining parts of the image. The definition uses the entire image as the context and agrees
with human intuition. It no longer relies on idealistic assumptions usually used before (e.g.
``background is homogenous") and generalizes well to complex objects and backgrounds in real
world images. To realize the definition, we illustrate how to incorporate different cues
such as appearance, position, and size. Based on a segment-based representation, the window
composition cost function can be efficiently evaluated by a greedy optimization algorithm.
Extensive evaluation on challenging object detection datasets verifies better efficacy and
efficiency of the proposed method comparing to the state-of-the-art, making it a good pre-processing
tool for subsequent applications. Moreover, we hope to stimulate further work towards the
challenging yet important problem of generic salient object detection.
Peng Wang, Jingdong Wang, Gang Zeng, Jie Feng
, Hongbin Zha, Shipeng Li.
Salient Object Detection for Searched Web Images via Global Saliency.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, 2012.
In this paper, we deal with the problem of detecting the existence and the location of salient objects for thumbnail images
on which most search engines usually perform visual analysis in order to handle web-scale
images. Different from previous techniques, such as sliding windowbased or segmentation-based
schemes for detecting salient objects, we propose to use a learning approach, random forest
in our solution. Our algorithm exploits global features from multiple saliency information
to directly predict the existence and the position of the salient object. To validate our
algorithm, we constructed a large image database collected from Bing image search, that contains
hundreds of thousands of manually labeled web images. The experimental results using this
new database and the resized MSRA database demonstrate that our algorithm outperforms previous