An object based auto annotation image retrieval system
Pei-Cheng Cheng, Been-Chian Chien, Hao‐Ren Ke, Wei‐Pang Yang · 2006
Abstract: In this paper, we proposed an auto annotation image retrieval system. In our system, an image was segmented into regions, each of which corresponds to an object. The regions identified by region-based segmentation are more consistent with human cognition than those identified by block-based segmentation. According to the object’s visual features (color and shape), new objects will be map to the similar clusters to obtain its associated semantic concept. The semantic concepts derived by the training images may not be the same as the real semantic concepts of the underlying images, because the former concepts depend on the low-level visual features. To ameliorate this problem, we propose a relevance-feedback model to learn the long-term and short-term interests of users.The experiments show that the proposed algorithm outperforms the traditional co-occurrence model about 19.5%; furthermore, after five times of relevance feedback, the mean average precision improves from 46 % to 62.7%.