Скачать или смотреть Automatic Image Tagging System, Hsiao-Tung Chen

Cornell University
School of Electrical and Computer Engineering
M.Eng Poster Session
May 5, 2015

Automatic Image Tagging System
Hsiao-Tung Chen, ECE M.Eng '15

Winner, AI & Pattern Recognition

Image tag annotations are an important component of searchable image databases such as Flickr, Picassa or Facebook. Such methods are becoming more and more important given the growing collections of user-provided visual content. Through image tagging we could build a complete image network and retrieve image more efficiently.

In the project, we build a system that allows a user to upload an image and subsequently returns a set of tags (or annotations) for the uploaded image tailored to that user. For the part of collecting tags, we propose to select the tags from images that are similar to input one. When a user upload an image to the platform, we extract bag-of-word, color histogram and other low level features from that image, and then use k-nearest neighbor search to find out most similar images within our database. Since the linear k-nearest neighbor construction has time complexity of O(n2). In order to make sure such an approach computationally feasible, we adopt some fast k-nearest neighbor search algorithm, including k-d tree, k-means tree.