Place recognition using kernel visual keyword descriptors

Visual Object and Place Recognition are very important issues in computer vision and mobile robotics. In the literature, many approaches have been introduced to solve these problems. Still, one of the most widely used approaches in computer vision refers to as combining the machine learning algorithms to learn objects for optimal recognition and also its image descriptors to describe the image content completely. Thus, in this way, the system is able to learn and describe the structural features of objects or places more effectively, which in turn; it leads to a correct recognition of objects. This paper introduces a method that uses the Kernel Principle Component Analysis (KPCA) approach to extract features from the visual scene. According to this approach, a set of SIFT features is extracted from a given image, and then, the minimum Euclidean distance between all local features is computed from the visual codebook which was constructed by K-means previously. The kernel analysis applied to the distance result to get better computational performance in recognition. SVM was used for data analysis and the results indicate that KPCA method significantly outperforms PCA and BOW approaches on Caltech-101 object dataset and IDOL visual place dataset.