Image Retrieval from Vague Description Based on AttnGAN
Rintaro Yanagi, Ren Togo, Takahiro Ogawa, Miki Haseyama · 2018
Image retrieval plays an important role in the information society. Many studies have been conducted to improve accuracy of the image retrieval. However, there exists a major limitation in their input methods. For example, if users only have a vague description that does not include detailed information such as its name and do not have an appropriate input image, it is difficult to retrieve their desired images. To solve this problem, we propose a novel image retrieval method that enables retrieval of a desired image from a vague description. In the proposed method, we generate a query image from a vague description through an Attentional Generative Adversarial Network. By using the generated query image, the proposed method enables users to retrieve images even if they do not have a clear retrieval description as an input. Experimental results show the effectiveness of our method.