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Boosting Contextual Information in Content-based Image Retrieval by Jaume Amores; Nicu Sebe; Petia Radeva; Theo Gevers; Arnold Smeulders is a scholarly article available to read on EtoBox.

What is Boosting Contextual Information in Content-based Image Retrieval about?

We present a new framework for characterizing and retrieving objects in cluttered scenes. This CBIR system is based on a new representation describing every object taking into account the local properties of its parts and their mutual spatial relations, without relying on accurate segmentation. For this purpose, a new multi-dimensional histogram is used that measures the joint distribution of local properties and relative spatial positions. Instead of using a single descriptor for all the image, we represent the image by a set of histograms covering the object from different perspectives. We integrate this representation in a whole framework which has two stages. The first one is to allow an efficient retrieval based on the geometric properties (shape) of objects in images with clutter. This is achieved by i) using a contextual descriptor that incorporates the distribution of local structures, and ii) taking a proper distance that disregards the clutter of the images. At a second stage, we introduce a more discriminative descriptor that characterizes the parts of the objects by their color and their local structure. By using relevant-feedback and boosting as a feature selection alg

Author

Jaume Amores; Nicu Sebe; Petia Radeva; Theo Gevers; Arnold Smeulders

Publisher

ACM

Published

2004

Language

EN