Point-based Medialness for Animal and Plant Identification

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Aparajeya, Prashant and Leymarie, Frederic Fol. 2014. 'Point-based Medialness for Animal and Plant Identification'. In: 1st International Workshop on Environnmental Multimedia Retrieval co-located with ACM International Conference on Multimedia Retrieval (ICMR 2014). Glasgow, United Kingdom. [Conference or Workshop Item]

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We introduce the idea of using a perception-based medial point description [9] of a natural form (2D static or in movement) as a framework for a part-based shape representation which can then be efficiently used in biological species identification and matching tasks. The first step is one of fuzzy medialness measurements of 2D segmented objects from intensity images which emphasises main shape information characteristics of an object’s parts (e.g. concavities and folds along a contour). We distinguish interior from exterior shape description. Interior medialness is used to characterise deformations from straightness, corners and necks, while exterior medialness identifies the main concavities and inlands which are useful to verify parts extent and reason about articulation and movement. In a second step we identify a set of characteristic features points built from three types. We define (i) an Interior dominant point as a well localised peak value in medialness representation, while (ii) an exterior dominant point is evaluated by identifying a region of concavity sub-tended by a minimum angular support. Furthermore, (iii) convex point are extracted from the form to further characterise the elongation of parts. Our evaluated feature points, together are sufficiently invariant to shape move ment, where the articulation in moving objects are characterised by
exterior dominant points. In the third step, a robust shape matching algorithm is designed that finds the most relevant targets from a database of templates by comparing the dominant feature points in a scale, rotation and translation invariant way (inspired by the SIFT method [17]). The performance of our method has been tested on several databases. The robustness of the algorithm is further tested by perturbing the data-set at different scales.

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Conference or Workshop Item (Paper)