Employing PLSA model and max-bisection for refining image annotation

We present a new method for refining image annotation by fusing probabilistic latent semantic analysis (PLSA) with max-bisection (MB). We first construct a PLSA model with asymmetric modalities to estimate the posterior probabilities of each annotating keyword for an image, and then a label similarity graph is built by a weighted linear combination of label similarity and visual similarity. Followed by the rank-two relaxation heuristics over the constructed label graph is employed to further mine the correlation of the keywords so as to capture the refining annotation, which plays a critical role in semantic based image retrieval. The novelty of our method mainly lies in two aspects: exploiting PLSA to accomplish the initial semantic annotation task and implementing max-bisection based on the rank-two relaxation algorithm over the weighted label graph to refine the candidate annotations generated by the PLSA. We evaluate our method on the standard Corel dataset and the experimental results are competitive to several state-of-the-art approaches.