A SIFT Based Object Recognition using contextual information

while automated object recognition in natural scenes has been studied for long times, it still remains a challenging problem in machine vision, image processing and analysis. Object recognition is classifying an unknown object into one of the set of specified categories. The main problem in object recognition begins from the several factors, scaling, rotation, distortion, illumination, occlusion etc. In this paper first, we apply Scale Invariant Feature Transform (SIFT) method on image to extract primitive points and the corresponding descriptors. Then we represent the extracted descriptors in form of AG graphs. So scene image and the model of objects we have two different graphs refer to it as scene and model graph. We suggest using the relaxation labeling to match the scene and model graphs. The result of experiment shows that the use of contextual information improves the descriptor matching significantly.