On Labeling Noise and Outliers for Robust Concept Learning for Image Databases

The mixture model for image databases remains as a challenging task since the database may contain clutter and outliers, and labelling information derived from multiple users may be inconsistent. Thus, neither the mixture model nor the labelling information is as ideal as most of the researchers have previously assumed. In this paper, we (a) address the problems of the noise disturbances for both mixture model and users' labelling information, (b) propose to process retrieval experiences in an intelligent manner using Bayesian analysis, (c) present a robust mixture model fitting algorithm to achieve visual concept learning, and (d) construct a concept-based indexing structure for efficient search of the database. The experimental results on a Corel image set show the correctness of our retrieval experience analysis, the effectiveness of the proposed concept learning approach, and the improvement of retrieval performance based on the indexing structure.