Object Type Classification Using Structure-based Feature Representation

Current feature-based object type classification methods information of texture and shape based information derived from image patches. Generally, input features, such as the aspect ratio, are derived from rough characteristics of the entire object. However, we derive input features from a parts-based representation of the object. We propose a method to distinguish object types using structure-based features described by a Gaussian mixture model. This approach uses Gaussian fitting onto foreground pixels detected by background subtraction to segment an image patch into several sub-regions, each of which is related to a physical part of the object. The object is modeled as a graph, where the nodes contain SIFT(Scale Invariant Feature Transform) information obtained from the corresponding segmented regions, and the edges contain information on distance between two connected regions. By calculating the distance between the reference and input graphs, we can use a k-NN-based classifier to classify an object as one of the following: single human, human group, bike, or vehicle. We demonstrate that we can obtain higher classification performance when using both conventional and structure-based features together than when using either alone.