Mining for video production invariants to measure style similarity | Zendy

Haidar Siba | Zendy; Joly Philippe | Zendy; Chebaro Bilal | Zendy

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Mining for video production invariants to measure style similarity

Author(s) -

Haidar Siba,

Joly Philippe,

Chebaro Bilal

Publication year - 2006

Publication title -

international journal of intelligent systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.291

H-Index - 87

eISSN - 1098-111X

pISSN - 0884-8173

DOI - 10.1002/int.20158

Subject(s) - similarity measure , invariant (physics) , similarity (geometry) , computer science , measure (data warehouse) , style (visual arts) , set (abstract data type) , similitude , pattern recognition (psychology) , artificial intelligence , algorithm , data mining , mathematics , image (mathematics) , programming language , geography , archaeology , mathematical physics

This article focuses on video document comparison using audiovisual production invariants (API). API are characterized by invariant segments obtained on a set of low‐level features. We propose an algorithm to detect production invariants throughout a collection of audiovisual documents. The algorithm runs on low‐level features, considered as time series, and extracts invariant segments using a one‐dimensional morphological envelop comparison. Then, based on the extracted results, we define a style similarity measure between two video documents. A derivative pseudo distance is also proposed. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 747–763, 2006.

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