Seeking an edge on Google in localized online search, Microsoft previewed on Monday a layered, geo-mapping service featuring aerial photographs and satellite images that it promised for later this year.
All the low-layer features are aggregated into several groups in a "bag of features" manner, and then a higher-layer feature is extracted based on the lower-layer features in the same group.
We propose a multi-layer feature extraction framework for speech, capable of providing invariant representations.
We propose a two-layer feature description structure that exploits the representation of spatio-temporal motion features and context features hierarchically.
Something distinct in the ground, such as a layer or feature that represents a definable unit of time, is known as a context; a grave, for example, might have a context number for the grave pit that was dug, another for the coffin and body placed there, and a third for the fill (the backfilling of the grave).
Each can be viewed as being composed of two layers, with a layer of feature generation and selection embedded within a discriminative method such as logistic regression, support vector machines (SVMs) or random forests.
While conventional intensity-based SD OCT demonstrated a continuous RPE layer feature in the majority of cases, polarization-sensitive OCT revealed distinct RPE discontinuities as well as RPE thinning/thickening within the lesion area.
The outer cookie layers feature a vanilla base, swirled and speckled with chocolate.
Mapbox then updates data and adds a layer of features on top of it.
The layered structure features a linear Mg3 cluster as node which is terminated by four coordinated DMA molecules.
In this paper, firstly, we proposed a kind of higher layer visualizing feature extraction algorithm based on the SAE model; then, we used the transfer learning methods to realize the global higher layer feature obtained from the small sample target objects in the sky based on the CNN model.
