Building up a Bio-Inspired Visual Attention Model by Integrating Top-Down Shape Bias and Improved Mean Shift Adaptive Segmentation

作者：Xu Jiawei^*; Yue Shigang

来源：International Journal of Pattern Recognition and Artificial Intelligence, 2015, 29(4): 1555005.

DOI：10.1142/S0218001415550058

• Visual attended objects
• adaptive segmentation
• top-down learning bias
• vehicle shape prior information
• bottom-up low level cues

The driver-assistance system (DAS) becomes quite necessary in-vehicle equipment nowadays due to the large number of road traffic accidents worldwide. An efficient DAS detecting hazardous situations robustly is key to reduce road accidents. The core of a DAS is to identify salient regions or regions of interest relevant to visual attended objects in real visual scenes for further process. In order to achieve this goal, we present a method to locate regions of interest automatically based on a novel adaptive mean shift segmentation algorithm to obtain saliency objects. In the proposed mean shift algorithm, we use adaptive Bayesian bandwidth to find the convergence of all data points by iterations and the k-nearest neighborhood queries. Experiments showed that the proposed algorithm is efficient, and yields better visual salient regions comparing with ground-truth benchmark. The proposed algorithm continuously outperformed other known visual saliency methods, generated higher precision and better recall rates, when challenged with natural scenes collected locally and one of the largest publicly available data sets. The proposed algorithm can also be extended naturally to detect moving vehicles in dynamic scenes once integrated with top-down shape biased cues, as demonstrated in our experiments.

• 出版日期2015-6