Mining spatial colocations from image-objects: A tensor factorization approach

The spatial colocation problem is totally different from the traditional association rule problem, as it operates on spatial data and not on conventional transaction data. In this work, a spatial colocation mining framework is proposed that mines spatial colocation of image-objects present in images using a tensor factorization approach. The framework takes in image data directly, tensorize it and perform the mining task, thus eliminating the need of converting into a transaction based approach. An interestingness measure called, spatial dominance is also proposed in this work. This measure is an indicator of the prevalence of the mined colocation pattern. Algorithms are designed in this framework, first to map the classified pixels as members of image-objects, which is a pre-stage before mining and second to find spatial colocation patterns. Experiment results are provided to show the strength of the spatial colocation mining algorithm.