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Adaptable models and semantic filtering for object recognition in street images

Qin, G and Vrusias, BL (2009) Adaptable models and semantic filtering for object recognition in street images In: ICSIPA '09, 2009-11-18 - 2009-11-19, Kuala Lumpur, Malaysia.

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Abstract

The need for a generic and adaptable object detection and recognition method in images, is becoming a necessity today, given the rapid development of the internet and multimedia databases in general. This paper compares the state-of-the-art in object recognition and proposes a method based on adaptable models for detecting thematic categories of objects. Furthermore, automatically constructed semantics are used for filtering false positive objects. The classification of objects into categories is performed by the popular Adaboost. The method has been used for identifying car objects and so far has indicated not only accurate recognition performance, but also good adaptability to new objects types.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Authors :
NameEmailORCID
Qin, GUNSPECIFIEDUNSPECIFIED
Vrusias, BLb.vrusias@surrey.ac.ukUNSPECIFIED
Date : 18 November 2009
Identification Number : https://doi.org/10.1109/ICSIPA.2009.5478683
Contributors :
ContributionNameEmailORCID
publisherIEEE, UNSPECIFIEDUNSPECIFIED
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 11:30
Last Modified : 17 May 2017 14:57
URI: http://epubs.surrey.ac.uk/id/eprint/831601

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