Multi-Model Fusion of Encoding Methods-Based Visual Words for Target Recognition in Infrared Images

Abstract

The limited information contained in infrared images present a serious problem, therefore it is necessary to form a powerful feature descriptor that allows extracting the maximum information and describing the image efficiently. To address this challenge, we propose a novel approach named multi-model fusion of encoding methods (MMFEM). First, several encoding methods for Bag Of Visual Words (BOVW) model were evaluated. Then, we fuse the best encoding methods obtained using three levels of fusion: feature-level fusion, decision-level fusion and hybrid-level fusion. Finally, the outputs of the fusion process were used to form a final decision for target recognition in infrared images. Two infrared datasets were employed to evaluate the performance of the proposed approach. The first one is Visible and Infrared Spectrum (VAIS) dataset comprising six categories of ships and the second dataset is a subset of Forward-Looking InfraRed (FLIR) thermal dataset comprising two object categories, vehicles and pedestrians. The proposed approach has exceed the state of the art for both datasets and we have reached 96.96% for FLIR and 71.26% for VAIS in overall classification accuracy.