We start by explaining exposure metering methods, describing types of signals that are used as the scene content descriptors as well as means to utilize these descriptors.
Section 3 discusses the types of signals that are used as the scene content descriptors as well as means to utilize these descriptors.
From this one can understand that for obtaining scene independent perceptual descriptors we need to remove the first two components.
CENTRIST is a visual feature descriptor for scene and object classification which performs a census transform (CT) of an image and replaces the image with its CT values [7].
A pool of landscape architects was shown test slides from that corridor, and was asked to rate each scene using the four descriptors.
To achieve these capabilities GeoPAT includes a library of scene signatures – compact numerical descriptors of patterns, and a library of distance functions – providing numerical means of assessing dissimilarity between scenes.
The final descriptor of the scene is eventually exploited by some pattern recognition algorithms to infer the scene category, skipping the recognition of the objects that are present in the scene [9].
According to the test results, it was found that the average error of using the ORB detector and descriptor for the scenes registration was around 0.24 meter.
The descriptor for each image scene is built in two stages.
Sound events are good descriptors for an auditory scene, as they help describing and understanding the human and social activities.
