Image categorization via robust pLSA
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › Not applicable › peer-review
Related Research Unit(s)
|Journal / Publication||Pattern Recognition Letters|
|Publication status||Published - 1 Jan 2010|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-70350621491&origin=recordpage|
This paper presents a novel method to give a good initial estimate of the probabilistic latent semantic analysis (pLSA) model using rival penalized competitive learning (RPCL), 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 (i.e., latent topics) in an unsupervised way using pLSA. Based on the discovered topics, image categorization is then carried out by ensemble-based support vector machine (SVM). We then find in the experiments that the pLSA model with RPCL initialization followed by ensemble-based SVM categorization is robust to the changes of the visual vocabulary and the number of latent topics. © 2009 Elsevier B.V. All rights reserved.
- Ensemble learning, Image categorization, Probabilistic latent semantic analysis, Rival penalized competitive learning