Applying local cooccurring patterns for object detection from aerial images

Publication Type:
Conference Proceeding
Citation:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2007, 4781 LNCS pp. 478 - 489
Issue Date:
2007-12-01
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Developing a spatial searching tool to enhance the search car pabilities of large spatial repositories for Geographical Information System (GIS) update has attracted more and more attention. Typically, objects to be detected are represented by many local features or local parts. Testing images are processed by extracting local features which are then matched with the object's model image. Most existing work that uses local features assumes that each of the local features is independent to each other. However, in many cases, this is not true. In this paper, a method of applying the local cooccurring patterns to disclose the cooccurring relationships between local features for object detection is presented. Features including colour features and edge-based shape features of the interested object are collected. To reveal the cooccurring patterns among multiple local features, a colour cooccurrence histogram is constructed and used to search objects of interest from target images. The method is demonstrated in detecting swimming pools from aerial images. Our experimental results show the feasibility of using this method for effectively reducing the labour work in finding man-made objects of interest from aerial images. © Springer-Verlag Berlin Heidelberg 2007.
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