Sentence examples for image representations from inspiring English sources

Further, we investigate if the extracted features from the employed image representations differ between different imaging modalities and if domain adaption helps to overcome these differences.

We first introduce the definitions of the data-driven forms of kernel and image representations.

Open image in new window Fig. 5 Two examples of image representations for patients.

In this step, we can learn sufficient mid-level image representations for location recognition.

Recently, implicit image representations attract much attention on describing local regions.

Disparity calculation results for the DRIVSCO sequence using different image representations.

In general, the image representations are high-dimensional, which makes the following analysis computationally more expensive.

Therefore, the concatenation of two image representations can achieve higher classification accuracy.

Fig. 3 Proposed framework for image retrieval based on individual image representations.

Figure 6 Annotation performance of csGM-LDA with different image representations.

Our proposed framework combines two kinds of image representations, therefore it needs more computational time.