Automatic image dataset construction with multiple textual metadata

Yazhou Yao, Jian Zhang, Fumin Shen, Xiansheng Hua, Jingsong Xu, Zhenmin Tang
2016 2016 IEEE International Conference on Multimedia and Expo (ICME)  
Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. AUTOMATIC IMAGE DATASET CONSTRUCTION WITH MULTIPLE TEXTUAL METADATA Anonymous ICME submission ABSTRACT The goal of this work is to automatically collect a large number of
more » ... ghly relevant images from the Internet for given queries. A novel image dataset construction framework is proposed by employing multiple textual metadata. In specific, the given query is first expanded by searching in the Google Books Ngrams Corpora to obtain a richer semantic description, from which the visually non-salient and less relevant expansions are then filtered. After retrieving the relevant images from the Internet, we further filter these noisy images by clustering and progressively Convolutional Neural Networks (CNN). To verify the effectiveness of our proposed method, we construct a dataset with 10 categories, which is not only much larger than but also have comparable cross-dataset generalization ability with the manually labelled dataset STL-10 and CIFAR-10. What's more, our method achieves a higher average precision than previous works.
doi:10.1109/icme.2016.7552988 dblp:conf/icmcs/YaoZSHXT16 fatcat:lmmpagvxmradbdplwafvr6feru