IMAGE TAG PAIR GRAPH FOR IMAGE ANNOTATION
First Claim
1. A method comprising:
- creating a plurality of image tag pairs, each image and tag pair from the plurality of image tag pairs comprising an image from a plurality of images and a tag from a plurality of tags, each pair from the plurality of image tag pairs being assigned a probability value, the probability value indicating a likelihood of a tag from a pair being descriptive of an image from the pair;
for each subject tag from the plurality of tags;
examining subject tag pairs for similarity between respective images, the subject tag pairs being those pairs from the plurality of image tag pairs that include the subject tag, andfor each of the subject tag pairs, adjusting its assigned probability value, based on a result of the examining of the subject tag pairs;
for each image from the plurality of images;
examining subject image pairs for correlation between respective tags, the subject image pairs being those pairs from the plurality of image tag pairs that include the subject image, andfor each of the subject image pairs, adjusting its assigned probability value, based on a result of the examining of the subject image pairs.
2 Assignments
0 Petitions
Accused Products
Abstract
An approach is described for automatically tagging a single image or multiple images. The approach, in one example embodiment, is based on a graph-based framework that exploits both visual similarity between images and tag correlation within individual images. The problem is formulated in the context of semi-supervised learning, where a graph modeled as a Gaussian Markov Random Field (MRF) is solved by minimizing an objective function (the image tag score function) using an iterative approach. The iterative approach, in one embodiment, comprises: (1) fixing tags and propagating image tag likelihood values from labeled images to unlabeled images, and (2) fixing images and propagating image tag likelihood based on tag correlation.
6 Citations
20 Claims
-
1. A method comprising:
-
creating a plurality of image tag pairs, each image and tag pair from the plurality of image tag pairs comprising an image from a plurality of images and a tag from a plurality of tags, each pair from the plurality of image tag pairs being assigned a probability value, the probability value indicating a likelihood of a tag from a pair being descriptive of an image from the pair; for each subject tag from the plurality of tags; examining subject tag pairs for similarity between respective images, the subject tag pairs being those pairs from the plurality of image tag pairs that include the subject tag, and for each of the subject tag pairs, adjusting its assigned probability value, based on a result of the examining of the subject tag pairs; for each image from the plurality of images; examining subject image pairs for correlation between respective tags, the subject image pairs being those pairs from the plurality of image tag pairs that include the subject image, and for each of the subject image pairs, adjusting its assigned probability value, based on a result of the examining of the subject image pairs. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
-
-
11. A computer-implemented system comprising:
-
an access module to access an image tag pair from a plurality of image tag pairs, each image tag pair from the plurality of image tag pairs comprising an image from a plurality of images and a tag from a plurality of tags, each pair from the plurality of image tag pairs being assigned a probability value, the probability value indicating a likelihood of a tag from a pair being descriptive of an image from the pair; a fixed tag module to iterate through tags in the plurality of tags and, for each subject tag from the plurality of tags; examine subject tag pairs for similarity between respective images, the subject tag pairs being those pairs from the plurality of image tag pairs that include the subject tag, and for each of the subject tag pairs, adjust its assigned probability value, based on a result of the examining of the subject tag pairs; a fixed image module to iterate through images in the plurality of images and for each image from the plurality of images; examine subject image pairs for correlation between respective tags, the subject image pairs being those pairs from the plurality of image tag pairs that include the subject image, and for each of the subject image pairs, adjust its assigned probability value, based on a result of the examining of the subject image pairs. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19)
-
-
20. A machine-readable non-transitory medium having instruction data to cause a machine to:
-
access an image tag pair from a plurality of image tag pairs, each image tag pair from the plurality of image tag pairs comprising an image from a plurality of images and a tag from a plurality of tags, each pair from the plurality of image tag pairs being assigned a probability value, the probability value indicating a likelihood of a tag from a pair being descriptive of an image from the pair; iterate through tags in the plurality of tags and, for each subject tag from the plurality of tags; examine subject tag pairs for similarity between respective images, the subject tag pairs being those pairs from the plurality of image tag pairs that include the subject tag, and for each of the subject tag pairs, adjust its assigned probability value, based on a result of the examining of the subject tag pairs; iterate through images in the plurality of images and, for each image from the plurality of images; examine subject image pairs for correlation between respective tags, the subject image pairs being those pairs from the plurality of image tag pairs that include the subject image, and for each of the subject image pairs, adjust its assigned probability value, based on a result of the examining of the subject image pairs.
-
Specification