Using the probabilistic latent semantic analysis method to Image categorization

This paper presents a method to give a good initial estimate of the probabilistic latent semantic analysis (PLSA) model using , since the expectation maximization EM) algorithm used to train the model is sensitive to the initialization. As a generative model from the statistical text literature, PLSA is further applied to the bag-of-words representation for each image in the database.Especially for those images containing multiple object categories (e.g. grass, roads, and buildings), we aim to discover the objects in an unsupervised way using PLSA. A burst of interest in image annotation and recommendation has been witnessed. Despite the huge effort made by the scientific community in the aforementioned research areas, accuracy or efficiency still remain open problems. resort to Probabilistic Latent Semantic Analysis (PLSA).

Using the probabilistic latent semantic analysis method to Image categorization Keywords:

Probabilistic Latent Semantic Analysis (PLSA) , Image categorization , EM algorithm , co-occurrence