Spatial String Matching for Image Classification
This paper presents a spatial string matching method to incorporate spatial information into the bag-of-words model, which represents an image as an unordered distribution of local features. Spatial constraints among neighboring features are explored in order to achieve better discrimination power for image classification. The features from neighboring points are combined together and taken as a spatial string, and then our method matches the images according to the similarity of string pairs. The categorization problem can be formulated using KNN or SVM classifier based on the spatial string matching kernel. The proposed method is able to capture spatial dependencies across the neighboring features. Experiment results show promising performance for image classification tasks.