Sentence examples for between the query image from inspiring English sources

In this framework, the similarities between the query image and dataset images are modeled as the likelihood and the relationships among the images in the dataset are modeled as the prior.

So, compute Manhattan distance between the query image and all images in database.

Based on d(Q,F), the server can identify the similarities between the query image and images in the image database.

After feature extraction step, Manhattan distances between the query image and database images are computed and final decision is made based on the proposed fuzzy system.

When a query image is inputted, a distance between the query image and the centroid is calculated, and then a distance is chosen from all the distances as the final score.

The distance between a query point and a data point defines the similarity between the query image and a database image.

The final hash distances between the query images and the original 50 images are shown for comparing the ordered random weighting and the unordered random weighting approaches.

For matching of SVT histograms between the query images and database images, we adopt the intersection kernel [40] due to its efficiency.

The histogram intersection distance evaluated for every bin n between the database image and the query image is given as D h Hd, Hq = ∑ n = 1 B min H d n, H q n min ∑ n = 1 B H d n, ∑ n = 1 B H q n, D h Hd, Hq ∈ 0, 13(13).

After that, the encrypted images with plaintext content similar to the query image are returned to the authorized user.

When receiving the encrypted query image, the server can calculate the distances between the encrypted query image and database images and then returns encrypted images similar to the query image in plaintext content, without knowing anything about the plaintext contents of the involved encrypted images.