Multi-feature Based Object Class Recognition

In object class recognition, lots of past researches focused on the local descriptors such as SIFT to categorize the variation of objects belonging to the same category in different poses, sizes, and appearance. However, SIFT descriptors may produce poor result especially if the object does not have enough information of its texture features. Due to this problem, we hypothesize that the use multi feature may increase the performance of object class recognition. In this paper, we use additional global shape features, Fourier Descriptors combined with SIFT descriptors to help in improving the performance of object class recognition. The selection of shape features is chosen due to the objects are easier to describe based on this features from human perspective compare to other features. We have divided our experiments into two: Experiment E1 is limited to the side view of bike, car, horse, and cow images whereas Experiment E2 consists of similar categories of dataset but in arbitrary views, rotations, and scales. The dataset we used in our experimentation are obtained from PASCAL, Weizmann and TU Darmstadt database. We assume that all objects are segmented manually before the feature extraction process. We validate our selection features using K-Means algorithm to evaluate the features for the purpose of object class recognition. Our results indicate that the combination of additional shape features together with SIFT descriptors performs better than using SIFT descriptors alone by up to 15% with limitation views of images.