Sentence examples for visualizing feature from inspiring English sources

We respectively carry out the higher layer visualizing feature extraction and lower layer visualizing feature extraction in the target object images.

The results of classification performance in two kinds of target objects under the lower layer visualizing feature extracted and higher layer visualizing feature extracted this paper proposed are listed in Table 1.

We adopt the color and texture of the lower layer visualizing feature to classify the different types of target object.

From Table 1, we can see the classification performance by using the higher layer visualizing feature extraction based on the SAE model is better than the traditional lower layer visualizing feature extraction, such as color and texture, which proved the accuracy of the higher layer visualizing feature extraction algorithm.

Table 1 Classification performance comparative results in two kinds of different target objects Visualizing feature extraction mode Classification performance Precision Recall Accuracy F1-Measure Lower layer visualizing feature Color 83.33 80 80.21 86 Texture 84.55 82 80.53 85 Higher layer visualizing feature paper proposed 91.75 84 82.15 87.

Because the sample numbers are enough, the classification precision under the SAE higher layer visualizing feature extraction based on non-transfer learning is slightly higher than transfer learning.

Through the image features transforming layer by layer, the sample feature of original space will be transformed to the new feature space, the higher layer visualizing features can be learnt, and the target detection and classification accuracy of the image are enhanced.

In this paper, a four-angle-star based visualized feature generation approach, FASVFG, is proposed to evaluate the distance between samples in a 5-class classification problem.

We present an integrative analysis tool to inter-connect disease subphenotypes and visualize feature intensity patterns.

This may be not only helpful to visualize feature representation but also contribute to analyze the reason of some incorrect classification results for designed feature.

Zeiler and Fergus [15] introduced the process of deconvolution as a technique to visualize features that are learned by a ConvNet.