Catalog Home Page

Automatic object detection using objectness measure

Shah, S.A.A., Bennamoun, M., Boussaid, F. and El-Sallam, A.A. (2013) Automatic object detection using objectness measure. In: 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA) 2013, 12-14 February 2013, Sharjah, United Arab Emirates

Link to Published Version: https://doi.org/10.1109/ICCSPA.2013.6487248
*Subscription may be required

Abstract

Object detection is an important step towards object recognition. A robust object detection system is one that can detect an object of any class. In this paper, we present a fully automatic approach to object detection based on an objectness measure. The proposed automatic object detection approach quantifies the likelihood for an image window to encompass objects in the image. It can discriminate between multiple objects in a scene, with individual windows capturing each detected object. Most importantly, the proposed approach does not require any manual input. We tested this approach on the challenging PASCAL VOC 07 dataset. Experimental results show that our approach provides a more accurate estimation of the required number of windows for an input image. The proposed technique is computationally efficient and takes less than 4 sec. per image.

Item Type: Conference Paper
URI: http://researchrepository.murdoch.edu.au/id/eprint/50511
Item Control Page Item Control Page