Salient foreground object detection based on sparse reconstruction for artificial awareness

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 object 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.