NDLI: Object Recognition by Combining Binary Local Invariant Features and Color Histogram

Content Provider	IEEE Xplore Digital Library
Author	Dung Phan Chi-Min Oh Soo-Hyung Kim In-Seop Na Chil-Woo Lee
Copyright Year	2013
Description	Author affiliation: Dept. of Electron. & Comput. Eng., Chonnam Nat. Univ., Gwangju, South Korea (Dung Phan; Chi-Min Oh; Soo-Hyung Kim; In-Seop Na; Chil-Woo Lee)
Abstract	In this paper, we propose an approach for object recognition using binary local invariant features and color information. In our approach, we use a fast detector for key point detection and binary local features descriptor for key point description. For local feature matching, the Fast library for Approximated Nearest Neighbors (FLANN) is applied to match the query image and reference image in data set. A homography matrix which represents transformation of object in scene image and reference image is estimated from matching pairs by using the Optimized Random Sample Consensus Algorithm (ORSA). Then, we detect object location in the image, and remove background of image. Next, significant color feature is used to calculate global color histogram since it reflects main content of primitive image and also ignores noises. Similarity of query image and reference object image is a linear combination of color histogram correlation and number of feature matches. As a result, the proposed method can overcome drawbacks of object recognition method using only local features or global features. In addition, the use of binary feature makes feature description as well as feature matching faster to meet the requirement of a real time system. For evaluation, we experiment with two well-known and latest local invariant features including the Oriented Fast and Rotated Binary Robust Independent Elementary Features (ORB) and Fast Retina Key point (FREAK) and a planar object data set. According to the result, ORB feature shows that it is powerful as our system obtained the higher accuracy and fast processing time. The experimental results also proved that combination of binary local invariant feature and significant color is effective for planar object recognition.
Starting Page	466
Ending Page	470
File Size	330167
Page Count	5
File Format	PDF
ISBN	9781479921904
DOI	10.1109/ACPR.2013.103
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-11-05
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Accuracy Image color analysis ORSA Lighting Binary local invariant feature Feature extraction Color histogram Robustness Homography Object recognition
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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