DocumentCode :
3673961
Title :
Recognizing cultural events in images: A study of image categorization models
Author :
Heeyoung Kwon;Kiwon Yun;Minh Hoai;Dimitris Samaras
Author_Institution :
Stony Brook University, NY 11794-4400, United States
fYear :
2015
fDate :
6/1/2015 12:00:00 AM
Firstpage :
51
Lastpage :
57
Abstract :
The goal of this work is to study recognition of cultural events represented in still images. We pose cultural event recognition as an image categorization problem, and we study the performance of several state-of-the-art image categorization approaches, including Spatial Pyramid Matching and Regularized Max Pooling. We consider SIFT and color features as well as the recently proposed CNN features. Experiments on the ChaLearn dataset of 50 cultural events, we find that Regularized Max Pooling with CNN, SIFT, and Color features achieves the best performance.
Keywords :
"Cultural differences","Image color analysis","Image recognition","Feature extraction","Support vector machines","Training","Visualization"
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition Workshops (CVPRW), 2015 IEEE Conference on
Electronic_ISBN :
2160-7516
Type :
conf
DOI :
10.1109/CVPRW.2015.7301336
Filename :
7301336
Link To Document :
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