UMP Institutional Repository

Object and Scene Category Recognition using a Combination of Dense Color SIFT Descriptors

Rassem, Taha H. and Khoo, Bee Ee and Bayuaji, Luhur and Makbol, Nasrin M. and Suryanti, Awang (2015) Object and Scene Category Recognition using a Combination of Dense Color SIFT Descriptors. Australian Journal of Basic and Applied Sciences, 9 (12). pp. 93-103. ISSN 1991-8178

[img] PDF
Restricted to Repository staff only

Download (607kB) | Request a copy


Nowadays, many features have been proposed for image category recognition. Scale Invariant Feature Transform (SIFT) is one of the important descriptors, which is used in these systems. It is robust against rotation change, viewpoint change, scaling change, but it is partially robust against illumination change. Color SIFT descriptors are proposed to increase the illumination invariant. In this paper, the performances of different color SIFT descriptors densely extracted from the images were evaluated for object and scene recognition. RGB color SIFT, HSV color SIFT, Opponent color SIFT, Transformed-color SIFT and a new proposed color SIFT descriptor based on Ohta color space (Ohta Color SIFT) were used instead of the traditional gray SIFT. The performances of these descriptors and all their possible combinations were evaluated using challenging data sets. Caltech-04, Caltech-101, Caltech-256, Graz-02 are examples of object data sets used, whereas Oliva and Torralba data set (OT) and SUN-398 are examples of scene data sets. Using some combination of dense color SIFT descriptors, remarkable results of classification accuracy were achieved for some data sets such as Caltech-04 and Graz-02 and acceptable accuracy results for the remaining data sets as shown in experimental results.

Item Type: Article
Uncontrolled Keywords: Image category recognition; SIFT; Color SIFT; Object data sets; Scene data sets
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Dr. Taha Hussein Alaaldeen Rassem
Date Deposited: 27 May 2015 03:59
Last Modified: 29 Mar 2018 08:02
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item