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Exact(5)

Chess dataset: image sequence 2, frame 960 vs frame 980.

Then, for each test image, we rotate it as the negative angle obtained using phase correlation technique to each dataset image.

Row 4: rice, rug, rye flakes, seeds 1, seeds 2. Row 5: sprinkles, floor tile, towel, wheat, wool fur Fig. 2 The texture dataset image acquisition setup.

The methodology employed in this paper differs from the one used by Pereira et al. [20], which optimized each dataset image individually, i.e., they aimed at fine-tuning LDOF for each image, being the final result the average over all images considering the AAE metric.

Using the conventional Hausdorff distance metric [14] as the image feature matching technique, the maximum local distance is chosen as the matching distance, which produces low distance even if every pair of image feature elements are close between a query image and a dataset image.

Similar(55)

For the best scenario (balanced dataset, images containing foreground animals only, and manually segmented) the accuracy reached a 88.9% Top-1 and 98.1% Top-5, respectively.

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.

In the off-line part, image representations of both BOW and VLAD are generated, and we further use BOW to construct an image graph, which is a structure containing relationships between the dataset images.

The resulting numbers are averaged over three dataset images.

In our approach, we have taken dataset images for testing.

Evaluation performed on H resolution training dataset images.

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