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|Title:||Generating representative views of landmarks via scenic theme detection|
Dirichlet Process Gaussian Mixture Model
Scenic Theme Detection
|Source:||Zhao, Y.-L.,Zheng, Y.-T.,Zhou, X.,Chua, T.-S. (2011). Generating representative views of landmarks via scenic theme detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6523 LNCS (PART 1) : 392-402. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-17832-0_37|
|Abstract:||Visual summarization of landmarks is an interesting and non-trivial task with the availability of gigantic community-contributed resources. In this work, we investigate ways to generate representative and distinctive views of landmarks by automatically discovering the underlying Scenic Themes (e.g. sunny, night view, snow, foggy views, etc.) via a content-based analysis. The challenge is that the task suffers from the subjectivity of the scenic theme understanding, and there is lack of prior knowledge of scenic themes understanding. In addition, the visual variations of scenic themes are results of joint effects of factors including weather, time, season, etc. To tackle the aforementioned issues, we exploit the Dirichlet Process Gaussian Mixture Model (DPGMM). The major advantages in using DPGMM is that it is fully unsupervised and do not require the number of components to be fixed beforehand, which avoids the difficulty in adjusting model complexity to avoid over-fitting. This work makes the first attempt towards generation of representative views of landmarks via scenic theme mining. Testing on seven famous world landmarks show promising results. © 2011 Springer-Verlag Berlin Heidelberg.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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