Visual place recognition is the task of recognizing the query image in a set of dataset images. It is a challenging problem in computer vision due to frequent and unpredictable environmental changes. In this paper, a novel approach is proposed for visual place recognition. We consider the problem of visual place recognition as a probabilistic voting problem on coherent image sequences. According to the co-visibility relationship of images in the dataset, each query can be represented by a categorical variable. Therefore, the whole sequence is the distribution of several independent and non-identical categorical variables. Introducing the probabilistic framework not only removes the need for heuristic parameters but also recognizes location efficiently and effectively. Two widely used datasets are used to evaluate the performance of the proposed method. The probabilistic voting algorithm achieves superior performance compared with state-of-the-art methods and satisfies the real-time requirement.