Content-based image retrieval
(CBIR) a technique for retrieving images on the basis of automatically-derived features such as colour, texture and shape.The issues discussed are color variance,
image compression and image database browsing.
Image compression, which is
usually used on most images, leads to both processing overheads but
also decreases retrieval performance.
Sensor responses that describe an image taken with a device
such as a digital color camera:
px = ex · nx ω Sx(λ)E(λ)R(λ)dλ
Where
λ: Wavelength
p: A 3-vector of sensor responses (RGB pixel values)
Sx: The surface reflectance at location x
E: The spectral power distribution of the illumination
R: The 3-vector of sensitivity functions of the device.
Exploring systems give an
outline of the image database to allow for intuitive navigation through the
image collection. This is particularly in the option when images are arranged according
to mutual similarity, that is, visually matching images are located close to
each other in the browsing interface.
Hue Sphere browsing system
provides a very efficient and effective approach to image database browsing. A
spherical visualization space is used, since users are familiar with the
concept of a globe and know how to navigate on one. Each image is identified by
its median color. However,instead of using RGB color space, HSV space is used as people find it more intuitive.
H values and V values are then mapped
to longitude and latitude values which point to the image thumbnail on the Hue sphere.
This approach is quick as the
co-ordinates are extracted directly from the images and more efficient and
faster than methods such as multi-dimensional scaling which rely on
computationally expensive iterative convergence procedure.
References:
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5466891
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