TY - GEN
T1 - Towards a universal and limited visual vocabulary
AU - Hou, Jian
AU - Feng, Zhan Shen
AU - Yang, Yong
AU - Qi, Nai Ming
PY - 2011
Y1 - 2011
N2 - Bag-of-visual-words is a popular image representation and attains wide application in image processing community. While its potential has been explored in many aspects, its operation still follows a basic mode, namely for a given dataset, using k-means-like clustering methods to train a vocabulary. The vocabulary obtained this way is data dependent, i.e., with a new dataset, we must train a new vocabulary. Based on previous research on determining the optimal vocabulary size, in this paper we research on the possibility of building a universal and limited visual vocabulary with optimal performance. We analyze why such a vocabulary should exist and conduct extensive experiments on three challenging datasets to validate this hypothesis. As a consequence, we believe this work sheds a new light on finally obtaining a universal visual vocabulary of limited size which can be used with any datasets to obtain the best or near-best performance.
AB - Bag-of-visual-words is a popular image representation and attains wide application in image processing community. While its potential has been explored in many aspects, its operation still follows a basic mode, namely for a given dataset, using k-means-like clustering methods to train a vocabulary. The vocabulary obtained this way is data dependent, i.e., with a new dataset, we must train a new vocabulary. Based on previous research on determining the optimal vocabulary size, in this paper we research on the possibility of building a universal and limited visual vocabulary with optimal performance. We analyze why such a vocabulary should exist and conduct extensive experiments on three challenging datasets to validate this hypothesis. As a consequence, we believe this work sheds a new light on finally obtaining a universal visual vocabulary of limited size which can be used with any datasets to obtain the best or near-best performance.
UR - https://www.scopus.com/pages/publications/80053349531
U2 - 10.1007/978-3-642-24031-7_40
DO - 10.1007/978-3-642-24031-7_40
M3 - 会议稿件
AN - SCOPUS:80053349531
SN - 9783642240300
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 398
EP - 407
BT - Advances in Visual Computing - 7th International Symposium, ISVC 2011, Proceedings
PB - Springer Verlag
T2 - 7th International Symposium on Visual Computing, ISVC 2011
Y2 - 26 September 2011 through 28 September 2011
ER -