Sentence examples for feature pool from inspiring English sources
The phrase "feature pool" is correct and can be used in written English
It can refer to a group or collection of prominent characteristics or attributes. It can also refer to a specific area or section of a larger pool, such as a designated area for certain activities or features. Example: The apartment complex boasts a luxurious feature pool, complete with a waterfall, hot tub, and slide.
Firstly, an algorithm for computing the Haar-like feature pool on a 32 × 32 grayscale image patch by using all simple and rotated Haar-like prototypes is introduced to represent a vehicle's appearance.
Then we design a novel feature selection model with group sparsity, Deep Sparse SVM (DSSVM) that not only can assign a suitable weight to the feature dimensions like the other traditional feature selection models, but also directly exclude useless features from the feature pool.
Then, sequential clustering is carried out on all features to give a feature pool.
We repeat the same procedure for all training samples in class c to create the feature pool F q c.
To start, a training sample from class c with all its features F = {f i } is randomly selected and added to the feature pool.
While creating feature pool, we also keep record of the percentage of sample images that contributed to feature f i which is denoted by r i.
These stages are measurement of a physical quantity relevant for visual quality, also called as feature, pooling the measured data over space and/or time, and mapping the pooled data to an estimate of perceived quality.
In this paper, we propose a novel spatial pooling method within an unsupervised feature learning framework, named Rich and Robust Feature Pooling (R2FP), to better extract rich and robust representation from sparse feature maps learned from the raw data.
For modeling spatiotemporal layout information embedded in facial expressions to improve recognition performance, we extend the idea of spatial pyramid matching (SPM) into video case, and perform spatiotemporal pyramid feature pooling following sparse coding feature extraction.
Some of these parties do feature pools, but in the wooded suburbs of the Valley, not chilly (in June, July and August) San Francisco.
The first class of models do not use PDs but involve complex image-based computing that includes feature extraction, feature pooling, and normalization [23], [24].
