Authors:
Jingyu Wang
1
;
Ke Zhang
1
;
Kurosh Madani
2
;
Christophe Sabourin
2
and
Jing Zhang
1
Affiliations:
1
Northwestern Polytechnical University, China
;
2
Université Paris-Est, France
Keyword(s):
Foreground Object Detection, Informative Saliency, Sparse Representation, Reconstruction Error, Artificial Awareness.
Related
Ontology
Subjects/Areas/Topics:
Image Processing
;
Informatics in Control, Automation and Robotics
;
Perception and Awareness
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
Artificial awareness is an interesting way of realizing artificial intelligent perception for machines. Since the foreground object can provide more useful information for perception and informative description of the environment than background regions, the informative saliency characteristics of the foreground object can be treated as a important cue of the objectness property. Thus, a sparse reconstruction error based detection approach is proposed in this paper. To be specific, the overcomplete dictionary is trained by using the image features derived from randomly selected background images, while the reconstruction error is computed in several scales to obtain better detection performance. Experiments on popular image dataset are conducted by applying the proposed approach, while comparison tests by using a state of the art visual saliency detection method are demonstrated as well. The experimental results have shown that the proposed approach is able to detect the foreground o
bject which is distinct for awareness, and has better performance in detecting the information salient foreground object for artificial awareness than the state of the art visual saliency method.
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