NDLI: Object recognition based on the region of interest and optical bag of words model

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Object recognition based on the region of interest and optical bag of words model

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Object recognition based on the region of interest and optical bag of words model

Content Provider	ACM Digital Library
Author	Dong, Peng Li, Weisheng
Abstract	To overcome the disadvantages of general object recognition methods based on traditional Bag of Words, this paper proposed a method for object recognition based on Region of Interest (ROI) and optimal Bag of Words model (BOW). Firstly, extracting the ROI, the interest image, whose features are detected and described using the Scale Invariant Feature Transform (SIFT). Secondly, constructing a visual codebook by clustering the feature vectors using K-means++ cluster algorithm. Thirdly, the mapping relationships between the vectors and visual codebook are computed to construct a visual word histogram that represents the image. Finally, The Support Vector Machine (SVM) is utilized to perform image classification and recognition. The experiments are performed on the MSRC 21-class database. The results show that the recognition accuracy of the proposed method is better than the traditional object recognition methods.
Starting Page	394
Ending Page	398
Page Count	5
File Format	PDF
ISBN	9781450322522
DOI	10.1145/2499788.2499873
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-08-17
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Region of interest K-means++ cluster Object recognition Bag of words model
Content Type	Text
Resource Type	Article

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