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

TL;DR

This paper introduces a novel object recognition method from unconstrained video sequences using descriptor transition tables, enabling retrieval from large, cluttered databases acquired with consumer mobile devices.

Contribution

The paper proposes a new recognition approach that models viewpoint changes with descriptor transition tables and Markov chains, suitable for mobile device video retrieval in cluttered environments.

Findings

Effective recognition despite background clutter

Handles large viewpoint variations

Validated on challenging mobile-acquired video dataset

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.