Feature Fusion in Improving Object Class Recognition

Abstract

Problem statement: Extraction of features in object class recognition researches previously gives attention to local features as discriminative features. This is because local features have invariant properties that are robust to viewpoints, translation and rotation. However this feature still has a limitation to represent high-level representation of objects. The problem will occur if the object is too small and do not have strong local features. Approach: This study proposes the combination of different features with local features for improving performance of object class recognition. The objective of this study is to address the problem of building object class representation based on these different features. The different features are sourced from boundary-based shape features. The dataset used consists of segmented objects with unrestricted poses and sizes from publicly image database. Both types of features are combined using feature fusion approach by concatenating those features in a new single feature vector. This new feature vector is trained by Support Vector Machine (SVM) to predict of unknown object class. Result/Conclusion: Experimental result show the inclusion of more than one type of features yields improvements of object class recognition compared to using single feature.