Sciweavers

ICASSP
2010
IEEE

Supervised topic model for automatic image annotation

13 years 3 months ago
Supervised topic model for automatic image annotation
This paper presents a new probabilistic model for the task of image annotation. Our model, which we 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, we 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, Srik
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where ICASSP
Authors Duangmanee Putthividhya, Hagai Thomas Attias, Srikantan S. Nagarajan
Comments (0)