Multi-view Landmark Recognition in Large-scale Image Collections
نویسنده
چکیده
From the large amount of web photos and other meta knowledge, recognizing landmark images has been actively studied in recent years. Previous landmark recognition research based on visual similarities have several limitations in terms of sparseness, efficiency and functionality. In this paper, I proposed a landmark hierarchy which consists of images, geographical clusters, visual clusters and topics and cluster images into the hierarchy to reduce sparseness in image retrieval for landmark recognition. Especially, I discovered topics from landmark visual clusters from multiple features such as texts and images. To discover hidden relationship between them, I applied multi-view topic analysis on landmark recognition, and inference topics using max-marginal approach. Moreover, to deal with million of images and their meta information, I have deployed whole clustering and multi-view learning procedure on distributed machines. Finally, I validate landmark clusters and topics in the experiment section.
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