Hue class equalization to improve a hierarchical image retrieval system

Tristan d'Anzi 1 William Puech 1 Christophe Fiorio 1 Jérémie François 1
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : This paper proposes a filtering system within a large database in order to accelerate image retrieval. A first filter is applied to the database in order to have a small number of candidates. This filter consists of a global descriptor based on color classification. Instead of the use of static classification based on the HVS (Human Visual System), the classification is based on a uniform repartition of pixels from the database. Those classes are gathered from a learning database. With this classification a global descriptor is computed based on hue, saturation and lightness. An equiprobability of each pixel is assigned to each class, this allows us to have a more constant reduction for the requested image and to have better filtering of the candidates. A more powerful and time consuming method can be used then for identifying the best candidate.
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Tristan d'Anzi, William Puech, Christophe Fiorio, Jérémie François. Hue class equalization to improve a hierarchical image retrieval system. IPTA: Image Processing Theory, Tools and Applications, Nov 2015, Orléans, France. pp.561-566, ⟨10.1109/IPTA.2015.7367210⟩. ⟨lirmm-01273971⟩

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