Semantic Indoor Scene Recognition of Time-Series Arial Images from a Micro Air Vehicle Mounted Monocular Camera

摘要

This paper presents a semantic scene recognition method from indoor aerial time-series images obtained using a micro air vehicle (MAV). Using category maps, topologies of image features are mapped into a low-dimensional space based on competitive and neighborhood learning. The proposed method comprises two phases: a codebook feature description phase and a recognition phase using category maps. For the former phase, codebooks are created automatically as visual words using self-organizing maps (SOMs) after extracting part-based local features using a part-based descriptor from time-series scene images. For the latter phase, category maps are created using counter propagation networks (CPNs) with extraction of category boundaries using a unified distance matrix (U-Matrix). With manual MAV operation, we obtained aerial time-series image datasets of five sets for two flight routes: a round flight route and a zigzag flight route. The experimentally obtained results with leave-one-out cross-validation (LOOCV) for datasets divided with 10 zones revealed respective mean recognition accuracies for the round flight datasets and zigzag flight datasets of 71.7% and 65.5%. The created category maps addressed the complexity of scenes because of segmented categories in both flight datasets.