基于ML-pLSA模型的目标识别算法

为了避免图像目标识别过程中识别结果对分割结果的过度依赖，该文提出了一种基于多尺度的概率潜在语义分析目标识别方法(Multi-Level-probabilistic Latent Semantic Analysis, ML-pLSA)。该方法利用多种分割算法对图像进行多尺度分割，再利用pLSA算法和词袋方法(Bag Of Words, BOW)对分割区域进行目标类别估计，最后联合多尺度的估计值给出最终分割结果。在目标尺度、目标角度、外界光照变化都相对较大的GRAZ-02数据库上进行实验，结果表明:与传统目标识别算法相比，该方法鲁棒性更强；在识别准确率方面，也有了很大的提高，取得了很好的效果。

A New Object Recognition Method Based on ML-pLSA Model

In order to avoid the condition that most of the segmentation based recognition methods have relied too much on the quality of image segments, a new object recognition method is proposed based on Multi-Level- probabilistic Latent Semantic Analysis (ML-pLSA) object recognition algorithm. Firstly, multiple segmentations at different levels are computed for each image, and then object classes on each segment region are estimated by using pLSA and Bag-Of-Words (BOW). The final results are obtained by fusing estimation results at multiple levels. The proposed algorithm is evaluated on Graz-02 dataset, a challenging dataset that contains large changes in object scale, object viewpoint and illumination condition. The experiment results demonstrate that the proposed method performs better than traditional object recognition methods in both accuracy and robustness.