Effective feature extraction by trace transform for insect footprint recognition

The paper discusses insect footprint recognition. Footprint segments are extracted from scanned footprints, and appropriate features are calculated for those segments (or cluster of segments) in order to discriminate species of insects. The selection or identification of such features is crucial for this classification process. This paper proposes methods for automatic footprint segmentation and feature extraction. First, we use a morphological method in order to extract footprint regions by clustering footprint patterns. Second, an improved SOM algorithm and an ART2 algorithm of automatic threshold selection are applied to extract footprint segments by clustering footprint regions regardless of footprint size or stride. Third, we use a trace transform technique in order to find out appropriate features for the segments extracted by the above methods. The trace transform builds a new type of data structure from the segmented images, by defining functions based on parallel trace lines. This new type of data structure has characteristics invariant to translation, rotation and reflection of images. This data structure is converted into triple features by using diametric and circus functions; the triple features are finally used for discriminating patterns of insect footprints. In this paper, we show that the triple features found by applying the proposed methods are sufficient to distinguish species of insects to a specified degree.