Image Feature Reduction through Spoiling: Its Application to Multiple Matched Filters for Focus of Attention

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In order to be of use to scientists, large image databases need to be analyzed to create a catalogue of the objects of interest. One approach is to apply a multiple tiered search algorithm that uses reduction techniques of increasing computational complexity to select the desired objects from the database. The first tier of this type of algorithm, which is often called a focus of attention (FOA) algorithm, selects candidate regions from the image data and passes them to the next tier of the algorithm. In this paper we present a new approach to FOA that employs multiple matched filters (MMF), one for each object prototype, to detect the regions of interest. The MMF are formed using k-means clustering on a set of example image patches identified by experts. An innovation of the approach is to radically reduce the dimensions of the feature space, used by the k-means algorithm, by spoiling the sample image patches. This research was motivated by the need to detect small volcanos in the MagelIan probe data from Venus. An empirical evaluation of the approach illustrates that MMF plus average filter perform better than a single matched filter for high true detection rates.