Combining Multiple Image Descriptions for Loop Closure Detection

The success of visual loop closure detection depends on the discrimination ability of the image descriptions. Different sources of image descriptions may carry complementary information as well as redundant information. Though integrating them properly can be beneficial, a main obstacle is the lack of analytical quality indicators to weigh different descriptions jointly. Inspired by the linear discriminant analysis, we propose an efficacy index to evaluate the weighted linear combinations of multiple image descriptions for loop closure detection. When a collection of image descriptions is given, the optimal weights maximizing the efficacy index are deduced analytically. As negative weights may negatively affect the performance of detection, a gradient descent algorithm is further proposed to jointly optimize the nonnegative weights. We use the proposed weighting strategies to combine the image descriptions extracted from multiple local image patches by multiple descriptor extractors. It is experimentally demonstrated that our weighted combinations of image descriptions can greatly improve the performance of loop closure detection by emphasizing informative components and de-emphasizing redundant components.