Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Efficient Color Histogram Indexing for Quadratic Form Distance Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Generating summaries and visualization for large collections of geo-referenced photographs
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Generating diverse and representative image search results for landmarks
Proceedings of the 17th international conference on World Wide Web
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This paper considers the problem of selecting representative photographs for regions in the worldwide dimensions. Selecting and generating such representative photographs for representative regions from large-scale collections would help us understand about local specific objects with a worldwide perspective. We propose a solution to this problem using a large-scale collection of geo-tagged photographs. Our solution firstly extracts the most relevant images by clustering and evaluation on the visual features. Then, based on geographic information of the images, representative regions are automatically detected. Finally, we select and generate a set of representative images for the representative regions by employing the Probabilistic Latent Semantic Analysis (PLSA) modelling. The results show the ability of our approach to generate region-based representative photographs.