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Object detection is a computer vision problem which caught a large amount of attention. But the candidate boundingboxes extracted from only image features may end up with false-detection due to the semantic gap between the top-down and the bottom up information. In this paper, we propose a novel method for generating object bounding-boxes proposals using the combination of eye fixation point, saliency detection and edges. The new method obtains a fixation orientated Gaussian map, optimizes the map through single-layer cellular automata, and derives bounding-boxes from the optimized map on three levels. Then we score the boxes by combining all the information above, and choose the box with the highest score to be the final box. We perform an evaluation of our method by comparing with previous state-ofthe art approaches on the challenging POET datasets, the images of which are chosen from PASCAL VOC 2012. Our method outperforms them on small scale objects while comparable to them in general.
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