Media Tag Recommendation Technologies
First Claim
1. A tag recommendation system comprising:
- a tag selection element operable to select a plurality of candidate media/tag pairs wherein a candidate tag of each of the candidate media/tag pairs exhibits raw co-occurrence with a target tag of a target media/tag pair;
a tag co-occurrence normalization element operable to process each of the plurality of candidate media/tag pairs against the target media/tag pair resulting in corresponding tag co-occurrence (“
TC”
) relevance measures wherein the TC relevance measures are in a semantic domain;
a visual language modeling element operable to generate a candidate visual language model (“
VLM”
) for each candidate tag of the plurality of candidate media/tag pairs and a target VLM for a target tag of the target media/tag pair, and further operable to process each of the candidate VLMs against the target VLM resulting in corresponding tag content correlation (“
TCC”
) relevance measures wherein such relevance measures are in a visual domain;
a visual correlation element operable to process each of the candidate VLMs against the target VLM resulting in corresponding image-conditioned tag correlation (“
ITC”
) relevance measures wherein the ITC relevance measures are in a visual domain; and
a multi-domain combining element operable to combine one or more TC tag lists indicated by the TC relevance measures with one or more TCC tag lists indicated by the TCC relevance measures and with one or more ITC tag lists indicated by the ITC relevance measures, and operable to form a list of recommended tags from the combined one or more TC tags and the one or more TCC tags and the one or more ITC tags.
2 Assignments
0 Petitions
Accused Products
Abstract
Technologies for recommending relevant tags for the tagging of media based on one or more initial tags provided for the media and based on a large quantity of other tagged media. Sample media as candidates for recommendation are provided by a set of weak rankers based on corresponding relevance measures in semantic and visual domains. The various samples provided by the weak rankers are then ranked based on relative order to provide a list of recommended tags for the media. The weak rankers provide sample tags based on relevance measures including tag co-occurrence, tag content correlation, and image-conditioned tag correlation.
-
Citations
20 Claims
-
1. A tag recommendation system comprising:
-
a tag selection element operable to select a plurality of candidate media/tag pairs wherein a candidate tag of each of the candidate media/tag pairs exhibits raw co-occurrence with a target tag of a target media/tag pair; a tag co-occurrence normalization element operable to process each of the plurality of candidate media/tag pairs against the target media/tag pair resulting in corresponding tag co-occurrence (“
TC”
) relevance measures wherein the TC relevance measures are in a semantic domain;a visual language modeling element operable to generate a candidate visual language model (“
VLM”
) for each candidate tag of the plurality of candidate media/tag pairs and a target VLM for a target tag of the target media/tag pair, and further operable to process each of the candidate VLMs against the target VLM resulting in corresponding tag content correlation (“
TCC”
) relevance measures wherein such relevance measures are in a visual domain;a visual correlation element operable to process each of the candidate VLMs against the target VLM resulting in corresponding image-conditioned tag correlation (“
ITC”
) relevance measures wherein the ITC relevance measures are in a visual domain; anda multi-domain combining element operable to combine one or more TC tag lists indicated by the TC relevance measures with one or more TCC tag lists indicated by the TCC relevance measures and with one or more ITC tag lists indicated by the ITC relevance measures, and operable to form a list of recommended tags from the combined one or more TC tags and the one or more TCC tags and the one or more ITC tags. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
-
-
10. A method performed in a computing environment of generating an image-conditioned tag correlation (“
- ITC”
) relevance measure, the method comprising;generating a target visual language model (“
VLM”
) for a target tag ti of a target media/tag pair;generating a candidate VLM for a candidate tag tj of a plurality of candidate media/tag pairs wherein the candidate tag tj co-occurs with the target tag ti of the target media/tag pair; defining a similarity between a target media instance of the target media/tag pair and a candidate media instance of the candidate media/tag pair as likelihood defining a distance between ti and tj that is the inter-product of two likelihood vectors of likelihood between a target media of the target media/tag pair and tags ti and tj; and defining the ITC relevance measure as the inverse of the distance wherein the ICT relevance measure is in a visual domain. - View Dependent Claims (11, 12, 13)
- ITC”
-
14. A method performed in a computing environment of combining multi-domain relevance measures provided by a plurality of weak rankers, the method comprising:
-
defining an instance space that is comprised of a plurality of sets, each set being a set of instances provided by a distinct weak ranker of a plurality of the weak rankers, each instance of a set of instances being a sample tag from a media/tag pair; generating a mapping function for each weak ranker, each mapping function operable to map a corresponding instance from the instance space to a ranking space R; and mapping each instance using the corresponding mapping function from the instance space to a ranking space R. - View Dependent Claims (15, 16, 17, 18, 19, 20)
-
Specification