Please use this identifier to cite or link to this item: https://doi.org/10.1109/TIP.2013.2272514
Title: Image classification via object-aware holistic superpixel selection
Authors: Wang, Z.
Feng, J.
Yan, S. 
Xi, H.
Keywords: holistic superpixel selection
Image classification
object-aware
region mergence
Issue Date: 2013
Source: Wang, Z.,Feng, J.,Yan, S.,Xi, H. (2013). Image classification via object-aware holistic superpixel selection. IEEE Transactions on Image Processing 22 (11) : 4341-4352. ScholarBank@NUS Repository. https://doi.org/10.1109/TIP.2013.2272514
Abstract: In this paper, we propose an object-aware holistic superpixel selection (HPS) method to automatically select the discriminative superpixels of an image for image classification purpose. Through only considering the selected superpixels, the interference of cluttered background on the object can be alleviated effectively and thus the classification performance is significantly enhanced. In particular, for an image, HPS first selects the discriminative superpixels for the characteristics of certain class, which can together match the object template of this class well. In addition, these superpixels compose a class-specific matching region. Through performing such superpixel selection for several most probable classes, respectively, HPS generates multiple class-specific matching regions for a single image. Then, HPS merges these matching regions into an integral object region through exploiting their pixel-level intersection information. Finally, such object region instead of the original image is used for image classification. An appealing advantage of HPS is the ability to alleviate the interference of cluttered background yet not require the object to be segmented out accurately. We evaluate the proposed HPS on four challenging image classification benchmark datasets: Oxford-IIIT PET 37, Caltech-UCSD Birds 200, Caltech 101, and PASCAL VOC 2011. The experimental results consistently show that the proposed HPS can remarkably improve the classification performance. © 2013 IEEE.
Source Title: IEEE Transactions on Image Processing
URI: http://scholarbank.nus.edu.sg/handle/10635/56246
ISSN: 10577149
DOI: 10.1109/TIP.2013.2272514
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

4
checked on Dec 5, 2017

WEB OF SCIENCETM
Citations

4
checked on Nov 2, 2017

Page view(s)

38
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.