Runtime Performance Enhancement of a Superpixel Based Saliency Detection Model

Reducing computational cost of image processing for various real time computer and robotic vision tasks, e.g. object recognition and tracking, adaptive compression, content aware image resizing, etc. remains a challenge. Saliency detection is often utilized as a pre-processing step for rapid, parallel, bottom-up processing of low level image features to compute saliency map. Subsequent higher level, complex computer vision tasks can then conveniently focus on identified salient locations for further image processing. Thus, saliency detection has successfully mitigated computational complexity of image processing tasks although processing speed enhancement still remains a desired goal. Recent fast and improved superpixel models are furnishing fresh incentive to employ them in saliency detection models to reduce computational complexity and enhance runtime speed. In this paper, we propose use of the superpixel extraction via energy driven sampling (SEEDS) algorithm to achieve processing speed enhancement in an existing saliency detection model. Evaluation results show that our modified model achieves over 60 % processing speed enhancement while maintaining accuracy comparable to the original model.