The experiments include using a single feature by non-optimal Gabor filters, using a single feature by optimized Gabor filters, and using a fusion.
Tracking of an object using a single feature or a technique often fails to achieve greater accuracy.
The motivation behind MKL is to combine or fuse multiple kernels or features rather than using a single feature representation to make prediction, with expectation that such combination leads to potential gain in performance.
In [34], we have shown that using two features, namely, received power and SINR from the MS, the classification accuracy is much better than using a single feature such as SINR [6, 35 37].
The proposed method can make use of different representations simultaneously (i.e., multiview learning) to obtain a better prediction performance than using a single feature representation (i.e., single-view learning) or a subset of features, with the advantage of automatic feature selections.
Such environments are usually uncontrolled in terms of lighting and color quality, as well as human expressivity and movement; as a result, using a single feature extraction technique may fail in some parts of a video sequence, while performing well in others.
(1) This method used a single feature extraction strategy.
It has been shown that using a single feature as the sole diagnostic criterion for AAB species definition is unreliable.
In comparison to the existing methods that employed multiple features, our SVM-based method by using a single feature (tri-nucleotide composition), achieved MCC of 0.98.
Each of those rules only uses a single feature and thus cannot deal with the mutual relationship between h and w directly.
