MDPV - Metric Distance Permutation Vocabulary

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Authors

DOHNAL Vlastislav HOMOLA Tomáš ZEZULA Pavel

Type Article in Periodical
Magazine / Source Information Retrieval
MU Faculty or unit

Faculty of Informatics

Citation
WWW http://link.springer.com/article/10.1007/s10791-014-9247-6
Doi http://dx.doi.org/10.1007/s10791-014-9247-6
Field Informatics
Keywords feature quantization; visual vocabulary; bag-of-features model; k-means clustering; metric distance permutation vocabulary
Description Sub-image content-based similarity search forms an important operation in current image archives since it provides users with images that contain a query image as their part. Such a search can conveniently be implemented using the bag-of-features model. Its integral part is a construction of visual vocabulary. Most existing algorithms to create a visual vocabulary suffer from high computational (e.g. k-means) or supervisor-guidance (e.g. visual-bit classifier, or sparse coding) requirements. In this paper, we propose a~novel approach to visual vocabulary construction called Metric Distance Permutation Vocabulary (MDPV). It is based on permutations of metric distances to create compact visual words. Its major advantage over prior techniques is time and space efficiency of vocabulary construction and quantization process during querying, while achieving comparable or even better effectiveness (query result quality). Moreover, this basic concept is extended to combine more independent permutations. Both the proposals are experimented on well-known real-world data-sets and compared to other state-of-the-art techniques.
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