A New Strategy of the Bag-of-Word Method with a Multi-scale Representation for Evaluating Color Pairwise Descriptors- Crimson Publishers
A New Strategy of the Bag-of-Word Method with a
Multi-scale Representation for Evaluating Color Pairwise Descriptors by Ahmed
Kaffel in Open Access Biostatistics & Bioinformatic: crimson publishers
open access journal- Crimson Publishers
In the field of image categorization, the
Bag-Of-Word has proved to be successful. It treats local image features as
visual words. After collecting all local features, each image is represented by
a histogram of occurrences of visual words. In this work, we propose an
extension to the Bag-Of-Words (BOW) by integrating the spatial relationships
information between local features. In a first step, we extract local features
by using both multi-scale representation and color descriptors based on
HSV-SIFT, opponent-SIFT, RGB-SIFT, rg-SIFT and transformed-color-SIFT. In a
second step, and in order to represent
the relationships between local features, we form pairwise color descriptors by
joining pairs of spatially neighbor SIFT color features. In a third step, we
encode the histograms which involve the occurrence of pairwise color
descriptors by applying the BOW strategy and the Spatial Pyramid Representation
(SPR). Finally, image classification is carried out by using Support Vector
Machine (SVM) on the generated histograms. Our proposed method is tested and
validated using the standard image datasets “Pascal Voc 2007”.
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