Metric learning for maximizing MAP and its application to content-based medical image retrieval

The descriptive power of low-level image features for describing the high-level semantic concepts is limited for content-based image retrieval (CBIR). To reduce this semantic gap and improve retrieval performance of CBIR, a distance metric learning method is proposed which can learn a linear projection to define a distance metric for maximizing mean average precision (MAP). The smooth approximation of MAP is optimized as the objective function by gradient-based approaches to find the optimal linear projection (called MPP). MPP is applied to retrieval of contrast-enhanced MRI images of brain tumors on a large dataset. The results demonstrate the effectiveness of MPP as compared to the state-of-the-art metric learning methods.