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The extracted features are then grouped to form a Bag of Features.
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.
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Such features include timbral texture features, rhythmic features, pitch content, or their combinations, yielding a bag-of-features (BOF) representation [1, 2, 6 18].
In course of robot's locomotion, salient visual features are detected and they shape a bag-of-features problem, quantized by a Neural Gas to code the spatial information for each scene.
Iveel et al. [29] extracted motion trajectories after video segment and build a bag-of-features (BOF) model based on four different types of descriptors (i.e., HOG, HOF, MBH, and TS).
The hierarchy was first computed using a divisive clustering algorithm and then represented a BOF-liked structure where each node was modeled by a bag-of-feature over MBH descriptors.
This histogram is a bag-of-features and is treated as a K-dimensional feature vector and to be recognized by a classifier.
For representing an entire image as a bag-of-features, the image is first decomposed into local parts in some way (the same technique was used for keypoint detection and description for image registration).
From the resulting tactile images, a bag-of-tactile features approach was implemented to achieve over 84% accuracy in recognition.
Recently, Niebles and Fei-Fei [4] enhance this approach by proposing a novel model characterized as a constellation of bags-of-features, which encodes both the shape and appearance of the actor.
Moreover, for person identification purposes, a bag-of-soft-biometric features is composed of different categories of features: color, texture, local features, and geometry.
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