CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2015-09-21
Cited: 2
Clicked: 6668
Fu-xiang Lu, Jun Huang. Beyond bag of latent topics: spatial pyramid matching for scene category recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(10): 817-828.
@article{title="Beyond bag of latent topics: spatial pyramid matching for scene category recognition",
author="Fu-xiang Lu, Jun Huang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="16",
number="10",
pages="817-828",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500070"
}
%0 Journal Article
%T Beyond bag of latent topics: spatial pyramid matching for scene category recognition
%A Fu-xiang Lu
%A Jun Huang
%J Frontiers of Information Technology & Electronic Engineering
%V 16
%N 10
%P 817-828
%@ 2095-9184
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500070
TY - JOUR
T1 - Beyond bag of latent topics: spatial pyramid matching for scene category recognition
A1 - Fu-xiang Lu
A1 - Jun Huang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
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SP - 817
EP - 828
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500070
Abstract: We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis (pLSA) based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ‘final’ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.
This paper proposed an approach to classifying image scenes in term of pLSA in the setting of spatial pyramid.
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