In the paper entitled "Face retrieval based on robust local features and statistical-structural learning approach," I. Defee and D. Zhong propose a framework for the unification of statistical and structural information for pattern retrieval based on local feature sets.
It combines the traditionally divergent statistical and structural models of texture analysis.
This description of patterns allows flexible varying of the amount of statistical and structural information; it can also be used with training process to optimize the retrieval performance.
A framework for the unification of statistical and structural information for pattern retrieval based on local feature sets is pre-sented.
The attention graphs allow for qualitatively characterizing the collective attention shift process, with evident statistical and structural changes under exogenous shocks.
Preliminary analysis of the results showed that the observed size effect can be considered as a combination of statistical and structural size effects.
These features are composed of a combination of statistical and structural ones, which are extracted over the columns and rows using a sliding window approach.
The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques.
Whereas Hawlick et al. [19] reported a survey for various statistical and structural features for image texture classification like autocorrelation function, optical transform, digital transform, textural edgeness, run length, gray tone co-occurrence, structural element, and autoregressive models.
To perform word recognition, the described system uses an original sliding window approach based on vertical projection histogram analysis of the word and extracts a new pertinent set of statistical and structural features from the word image.
