Automatic image annotation via category labels

Abstract

Automatic image annotation aims to assign relevant keywords to images and has become a research focus. Although many techniques have been proposed to solve this problem in the last decade, giving promissing performance on standard datasets, we propose a novel automatic image annotation technique in this paper. Our method uses a label transfer mechanism to automatically recommend those promising tags to each image by using the category information of images. As image representation is one of the key technique in image annotation, we use sparse coding based spatial pyramid matching and deep convolutional neural networks to model image features. And metric learning technique is further used to combine these features to achieve more effective image representation in this paper. Experimental results illustrate that the proposed method get similar or better results than the state-of-the-art methods on three standard image datasets.