Scene and place recognition using a hierarchical latent topic model

Place classification and object categorization are necessary functions of vision-based robotic systems. In this paper, a novel latent topic model is proposed to learn and recognize scenes and places. First, each image in the training set is characterized by a collection of local features, known as codewords, obtained by unsupervised learning, and each codeword is represented as part of a topic. Then, the codeword distribution of detected local features from the training images is learned by performing a k-means algorithm. Next, a modified Latent Dirichlet Allocation model is employed to highlight the significant features (i.e., the codewords with higher frequency in the codebook). The Highlighted Latent Dirichlet Allocation (HLDA) improves the efficiency of learning procedure. Finally, a fast variational inference algorithm for HLDA is proposed to reduce the computational complexity in parameter estimation. Experimental results using natural scenes, indoor and outdoor datasets show that the proposed HLDA method performs better than other counterparts in terms of accuracy and robustness with the variation of illumination conditions, perspectives, and scales. The Fast HLDA is order of magnitudes faster than the HLDA without obvious loss of accuracy.