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Visual recognition and vision based retrieval of objects from large databasesare tasks with a wide spectrum of potential applications. In this paper wepropose a novel recognition method from video sequences suitable for retrievalfrom databases acquired in highly unconstrained conditions e.g. using a mobileconsumer-level device such as a phone. On the lowest level, we represent eachsequence as a 3D mesh of densely packed local appearance descriptors. Whileimage plane geometry is captured implicitly by a large overlap of neighbouringregions from which the descriptors are extracted, 3D information is extractedby means of a descriptor transition table, learnt from a single sequence foreach known gallery object. These allow us to connect local descriptors alongthe 3rd dimension (which corresponds to viewpoint changes), thus resulting in aset of variable length Markov chains for each video. The matching of two setsof such chains is formulated as a statistical hypothesis test, whereby a subsetof each is chosen to maximize the likelihood that the corresponding videosequences show the same object. The effectiveness of the proposed algorithm isempirically evaluated on the Amsterdam Library of Object Images and a newhighly challenging video data set acquired using a mobile phone. On both datasets our method is shown to be successful in recognition in the presence ofbackground clutter and large viewpoint changes.

Descriptor transition tables for object retrieval using unconstrained cluttered video acquired using a consumer level handheld mobile device