Improving image similarity measures for image browsing and retrieval through latent space learning between images and long texts

The amount of multimedia data on personal devices and the Web is increasing daily. Image browsing and retrieval systems in a low-dimensional space have been widely studied to manage and view large numbers of images. It is essential for such systems to exploit an efficient similarity measure of the images when searching for them. Existing methods use the distance in a low-level image feature space as the similarity measure, and therefore, images with different content may be treated as similar images. In this paper, we propose a novel method to improve the similarity measures for images by considering the text surrounding the images. If there is text describing the images, similarities can be measured more effectively by taking into account the text streams. The proposed method improves the image similarity measures based on the latent semantics obtained from the combination of image and text. It should be noted that the text does not need to be clear tags; indeed, any generic Web text is applicable. Moreover, our method can effectively improve the similarities even if only a small portion of the images include textual descriptions. Additionally, the proposed method is scalable as it has linear computational complexity based on the number of images. In the experiments, we compare our method with previous methods using an original dataset in which a portion of the images are annotated by long text. We show that the proposed method can retrieve semantically similar images more precisely than existing methods.