基于随机游走模型的物体识别

针对传统物体识别算法中只依赖于视觉特征进行识别的单一性缺陷，提出了一种结合先验关系的物体识别算法。在训练阶段，通过图模型结构化表示先验关系，分别构建了图像-图像、语义-语义两个子图以及两子图之间的联系，利用该图模型建立随机游走模型；在识别阶段，建立待识别图像与随机游走模型中的图像节点和语义节点的关系，在该概率模型上进行随机游走，将随机游走的结果作为物体识别的结果。实验结果证明了结合先验关系的物体识别算法的有效性；提出的物体识别算法具有较强的识别性能。

Random walk model based object recognition

Traditional object recognition methods in computer vision are almost based on the visual features, which cannot perform well in a more complex circumstance. To attack this critical problem, this paper proposes a novel object recognition method which combines object recognition with the prior relations. During the training stage, structured presentation of the prior relations is applied through a hybrid graph which contains image similar sub-graph, semantic similar sub-graph and the relations between the two sub-graphs. A random walk model is then constructed according to the hybrid graph. During the recognition stage, a new testing image node is added to the random walk model. The relations between this node and the nodes in the random walk model are calculated. Random walks which start from the testing image node are performed at the random walk model. The probability rank provided by the result of random walks will serve as the recognition result of the testing image. Experimental results illustrate the validity and stronger recognition performance of the proposed method.