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The authors in [ [16]] performed continuous-scale emotion tracking in movies, fusing features from audio, music, and video modalities.
Here we proposed a novel approach to predict the subcellular localization of bacterial proteins by fusing features from position-specific score matrix (PSSM), Gene Ontology (GO) and PROFEAT.
Since evidence theory has shown high efficiency in handling uncertain information, an evidence fusion-based framework for condition evaluation has been presented in this paper to improve the certainty and precision of evaluation decisions by fusing features extracted from different sources of evidence.
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Zhou and Yang [20] have proposed fusing feature Fisher classifier (F3C) approach where the face image is first divided into smaller subimages and then the discrete cosine transform (DCT) technique is applied to the whole image and some subimages to extract the holistic and local facial features.
With SVM, the fused features are used to discriminate four different types of heartbeats.
First, calculate the Euclidean distance between fused features and select the best 500 features based on minimum distance.
Multiple kernel learning (MKL) methods are widely adopted to learn the feature weights and to fuse features on score-level.
Sharan et al. [20 22] combined reflectance and 3D texture into new fused features as input to an LDA or an SVM classifier.
Based on these works, a CBIR system is designed using color and texture fused features by constructing weights of feature vectors.
The relevant retrieval experiments show that the fused features retrieval brings better visual feeling than the single feature retrieval, which means better retrieval results.
Subsequently, the efficient RWN is applied to classify these fused features to further improve the recognition rate and the recognition speed.
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