Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a new probabilistic model for the task of image annotation. Our model, which  call sLDA-bin, extends supervised Latent Dirichlet Allocation (sLDA) model to handle a multi-variate binary response variable of the annotation data. Unlike correspondence LDA (cLDA), the association model in sLDA allows each caption word to be associated with more than 1 image region and is thus more appropriate for annotation words that globally describe the scene. By modeling the response variable as a multi-variate Bernoulli,  introduce a tight convex variational bound for the logistic function and derive an efficient variational inference algorithm based on mean-field approximation. Our model compares favorably with cLDA on an image annotation task, as demonstrated by a superior caption prediction probability.
['Duangmanee Putthividhya', 'Hagai Thomas Attias', 'Srikantan S. Nagarajan']
I am writing a research paper on a idea Supervised topic model for automatic image annotation
[{"from": "human", "value": "I am writing a research paper on a idea Supervised topic model for automatic image annotation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a new probabilistic model for the task of image annotation. Our model, which  call sLDA-bin, extends supervised Latent Dirichlet Allocation (sLDA) model to handle a multi-variate binary response variable of the annotation data. Unlike correspondence LDA (cLDA), the association model in sLDA allows each caption word to be associated with more than 1 image region and is thus more appropriate for annotation words that globally describe the scene. By modeling the response variable as a multi-variate Bernoulli,  introduce a tight convex variational bound for the logistic function and derive an efficient variational inference algorithm based on mean-field approximation. Our model compares favorably with cLDA on an image annotation task, as demonstrated by a superior caption prediction probability."}]