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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.
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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In addition, decoding times increase only sightly due to the compact representation of fused feature sets.
The fused feature-based translation estimation and the scale estimation are separately processed in SAMF.
Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules.
This process continues up to d features, where d denotes the dimension of fused feature vector, which is 1 × 3863.
Finally, the object locations are efficiently predicted by applying the multi-channel correlation filter on the fused feature maps.
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