Salient object subitizing
Files
Accepted manuscript
Date
2017-09-01
Authors
Zhang, Jianming
Ma, Shugao
Sameki, Mehrnoosh
Sclaroff, Stan
Betke, Margrit
Lin, Zhe
Shen, Xiaohui
Price, Brian
Mech, Radomir
Version
Accepted manuscript
OA Version
Citation
Jianming Zhang, Shugao Ma, Mehrnoosh Sameki, Stan Sclaroff, Margrit Betke, Zhe Lin, Xiaohui Shen, Brian Price, Radomir Mech. 2017. "Salient Object Subitizing." INTERNATIONAL JOURNAL OF COMPUTER VISION, Volume 124, Issue 2, pp. 169 - 186 (18).
Abstract
We study the problem of salient object subitizing, i.e. predicting the existence and the number of salient objects in an image using holistic cues. This task is inspired by the ability of people to quickly and accurately identify the number of items within the subitizing range (1–4). To this end, we present a salient object subitizing image dataset of about 14 K everyday images which are annotated using an online crowdsourcing marketplace. We show that using an end-to-end trained convolutional neural network (CNN) model, we achieve prediction accuracy comparable to human performance in identifying images with zero or one salient object. For images with multiple salient objects, our model also provides significantly better than chance performance without requiring any localization process. Moreover, we propose a method to improve the training of the CNN subitizing model by leveraging synthetic images. In experiments, we demonstrate the accuracy and generalizability of our CNN subitizing model and its applications in salient object detection and image retrieval.