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

Warren Rieutort-Louis, Ognjen Arandelovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Visual recognition and vision based retrieval of objects from large databases are tasks with a wide spectrum of potential applications. In this paper we propose a novel recognition method from video sequences suitable for retrieval from databases acquired in highly unconstrained conditions e.g. using a mobile consumer-level device such as a phone. On the lowest level, we represent each sequence as a 3D mesh of densely packed local appearance descriptors. While image plane geometry is captured implicitly by a large overlap of neighbouring regions from which the descriptors are extracted, 3D information is extracted by means of a descriptor transition table, learnt from a single sequence for each known gallery object. These allow us to connect local descriptors along the 3rd dimension (which corresponds to viewpoint changes), thus resulting in a set of
variable length Markov chains for each video. The matching of two sets of such chains is formulated as a statistical hypothesis test, whereby a subset of each is chosen to maximize the likelihood that the corresponding video sequences show the same object. The effectiveness of the proposed algorithm is empirically evaluated on the Amsterdam Library of Object Images and a new highly
challenging video data set acquired using a mobile phone. On both data sets our method is shown to be successful in recognition in the presence of background clutter and large viewpoint changes.
Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
Pages3030-3037
DOIs
Publication statusPublished - 3 Nov 2016
EventIEEE World Congress on Computational Intelligence - Vancouver, Canada
Duration: 24 Jul 201629 Jul 2016
http://www.wcci2016.org/

Conference

ConferenceIEEE World Congress on Computational Intelligence
Country/TerritoryCanada
CityVancouver
Period24/07/1629/07/16
Internet address

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