By Topic

Semi-Automatically Labeling Objects in Images

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wen Wu ; Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA ; Jie Yang

Labeling objects in images plays a crucial role in many visual learning and recognition applications that need training data, such as image retrieval, object detection and recognition. Manually creating object labels in images is time consuming and, thus, becomes impossible for labeling a large image dataset. In this paper, we present a family of semi-automatic methods based on a graph-based semi-supervised learning algorithm for labeling objects in images. We first present SmartLabel that proposes to label images with reduced human input by iteratively computing the harmonic solutions to minimize a quadratic energy function on the Gaussian fields. SmartLabel tackles the problem of lacking negative data in the learning by embedding relevance feedback after the first iteration, which also leads to one limitation of SmartLabel-needing additional human supervision. To overcome the limitation and enhance SmartLabel, we propose SmartLabel-2 that utilizes a novel scheme to sample negative examples automatically, replace regular patch partitioning in SmartLabel by quadtree partitioning and applies image over-segmentation (superpixels) to extract smooth object contours. Evaluation on six diverse object categories have indicated that SmartLabel-2 can achieve promising results with a small amount of labeled data (e.g., 1%-5% of image size) and obtain close-to-fine extraction of object contours on different kinds of objects.

Published in:

IEEE Transactions on Image Processing  (Volume:18 ,  Issue: 6 )