Building holistic descriptors for scene recognition

Real-world scene recognition has been one of the most challenging research topics in computer vision, due to the tremendous intraclass variability and the wide range of scene categories. In this paper, we successfully apply an evolutionary methodology to automatically synthesize domain-adaptive holistic descriptors for the task of scene recognition, instead of using hand-tuned descriptors. We address this as an optimization problem by using multi-objective genetic programming (MOGP). Specifically, a set of primitive operators and filters are first randomly assembled in the MOGP framework as tree-based combinations, which are then evaluated by two objective fitness criteria i.e., the classification error and the tree complexity. Finally, the best-so-far solution selected by MOGP is regarded as the (near-)optimal feature descriptor for scene recognition. We have evaluated our approach on three realistic scene datasets: MIT urban and nature, SUN and UIUC Sport. Experimental results consistently show that our MOGP-generated descriptors achieve significantly higher recognition accuracies compared with state-of-the-art hand-crafted and machine-learned features.