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In this paper, we describe a novel local descriptor of image texture representation for visual recognition.
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where N is the number of patches in an image, v i is the descriptor of an image patch, D is the Euclidean distance, and I is the identity function.
Ojala et al. [20] used LBP as a texture descriptor of the image as shown in Figure 2, which is composed by central pixel and neighborhoods.
The resultant histogram H is the LBP descriptor of that image.
The resulting aggregated representation is then compressed by PCA to obtain the final compact descriptor of the image.
This paper presents an image retrieval framework that uses affine image moment invariants as descriptors of local image areas.
As an extension to local descriptors' representation of images, probabilistic aspect models have been recently proposed to capture descriptors co-occurrence information with the use of a hidden variable (latent aspect).
With our previous approach, however, HD4AR matched feature descriptors of the image to an entire set of feature descriptors from all base images, which incurs unnecessary descriptor comparisons.
The present system is mainly developed based on the visual descriptors of the image such as color, texture and shape descriptors, etc. along with the high-level semantic analysis of the image content through different processing modules in the proposed architecture.
In our experiment, the descriptors of each image are 50,000 dimensions and are then reduced to 4096 dimensions by PCA.
HD4AR only compares feature descriptors of the image to that of each 3D point in the point cloud to find 2D-to-3D correspondences.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com