Multi-model classification method in heterogeneous image databases

Automatic heterogeneous image recognition is a challenging research topic in computer vision. In fact, a general purpose images require multiple descriptors to cover their diverse category contents. However, not all extracted features are always relevant. Furthermore, simply concatenating multiple features may not be efficient for recognizing images in heterogeneous databases. In this context, we propose a new heterogeneous image recognition system, which aims to enhance the recognition results while decreasing the computational complexity. In particular, the proposed system is based on two complementary methods: adaptive relevant feature selection and multi-model classification method (MM-CM). Since it employs hierarchically selected features, the MM-CM ensures better classification accuracy and significantly less computation time than existing classification methods. The performance of the proposed image recognition system is assessed through two image databases and a large number of features. A comparison with competing algorithms from the literature is also provided. Our extensive experimental results show that an adaptive feature selection based MM-CM outperforms existing methods and improves the classification results in heterogeneous image databases.