Sentence examples for visual word of from inspiring English sources

"Word of mouth is great but visual word of mouth is better, and that's how we got down this path".

We compute the spatio-temporal distance from the i-th combined visual word of fea1 to all the other words of fea2 by {mathcal{D}}_i^{mathrm{fea}1}=frac{{displaystyle {sum}_j{mathrm{DM}}_vleft i,jright)}}{{displaystyle {sum}_i{displaystyle {sum}_j{mathrm{DM}}_vleft i,jright)}}} (9).

We also tested the impact of the number of visual words on the results of the model by training the network using sparse SURF features on the DR2 dataset while varying M (Fig. 3).

Let's say two features named fea1 and fea2, and the visual words of these two features are ( left{{W}_i^{fea1}left|1le ile {k}^{fea1}right.right} ) and ( left{{W}_i^{fea2}left|1le ile {k}^{fea2}right.right} ), k fea1 and k fea2 stand for the amount of visual words in fea1 and fea2.

Symmetrically, the contingent probability that fea2's combined visual word ( {W}_j^{mathrm{fea}2} ) related to all the combined visual words of fea1 is Pleft({mathrm{mathcal{L}}}_s,:Big|{W}_j^{mathrm{fea}2}right)=frac{mathrm{His}left({W}_j^{mathrm{fea}2},{mathrm{mathcal{L}}}_sright)}{{displaystyle {sum}_smathrm{His}left({W}_j^{mathrm{fea}2},{mathrm{mathcal{L}}}_sright)}} (13).

This is likely to happen when a few visual words of SVT origin from the cluttered background, and the local SIFT descriptors of the query image may have high occurrences over these visual words, resulting in high Hausdorff distances to the database images which have the same foreground objects but different backgrounds.

Symmetrically, we also compute the spatio-temporal distance from the i-th combined visual words of fea2 to all the other words of fea1 by {mathcal{D}}_j^{mathrm{fea}2}=frac{{displaystyle {sum}_i{mathrm{DM}}_vleft i,jright)}}{{displaystyle {sum}_i{displaystyle {sum}_j{mathrm{DM}}_vleft v,jright)}}} (10).

Figure 6 overlays the top weighted visual words of the two categories over the Add2 ISH image.

Usually the codebook is established by conducting the clustering algorithms on a subset of the local features, and the cluster centers are then chosen as the visual words of the codebook.

More recent studies introduced filter banks and visual words for extraction of features.

Figure 5 shows three visual words for each of the four scales, selected as the visual words that contributed most to classification.