Getting Rid of Night: Thermal Image Classification Based on Feature Fusion

Abstract

Thermal images are essential to deal with situations in dark environments, as they capture the objects' temperature. While the objects can still be seen in thermal images, the texture is extremely blur or even not observable at all. We propose to extract different features from images that capture various characteristics of the images. As one feature emphasizes one distinguishing aspect differing from the others, we can grasp multiple pieces of evidence from the images and take advantage of each to improve the thermal image classification accuracy. In particular, in additional to corner features usually used in color images, we also extract features from the edges and the shapes of the objects that emphasize the integral image appearance, as well as the temperature characteristics obtained from the image intensity. In this way, even if one feature is not evident in an image, the others can still play a critical role towards the correct classification result. By optimizing the objective function, we maximize the fusion performance of multiple features. By doing so, we can to the largest extent make use of the information exhibited in the thermal images to classify the query image into the correct group. Experiments demonstrate promising thermal image classification result.