A New CNN-RNN Framework For Remote Sensing Image Captioning
Genc Hoxha, Farid Melgani, Jacopo Slaghenauffi
Abstract
Remote sensing (RS) image captioning has been recently attracting the attention of the community as it provides more semantic information with respect to the traditional tasks such as scene classification. Image captioning aims to generate a coherent and comprehensive description that summarizes the content of an image. The description can be obtained directly from the ground truth descriptions of similar images (retrieval based image captioning) or can be generated through the encoder-decoder framework. The former has the limitation of not generating new descriptions. The latter may be affected by misrecognition of scenes or semantic objects. In this paper we try to address these issues by proposing a new framework which is a combination of generation and retrieval based image captioning. First a CNN-RNN framework combined with beam-search generates multiple captions for a target image. Then the best caption is selected on the basis of its lexical similarity with the reference captions of most similar images. Experimental results on RSCID dataset are reported and discussed.