A Hybrid system with what-where-memory for multi-object recognition

To improve the efficiency of multi-object recognition in complex scenes, a hybrid system is proposed to learn the concurrencies and spatial relationships among different objects, and to apply such relationships for better recognitions. The hybrid system includes a bottom-up saliency map to generate regions of interest (ROIs), an independent classifiers to recognize these ROIs based on object appearances, and a what-where-memory (WWM) to cast the top-down knowledge of object relationships to help the recognitions provided by independent classifiers. The WWM learns not only the concurrencies but also the spatial layouts of different classes, which can filter out the classes that unlikely appear, and distinguish the correct class from ambiguous classes provided by independent classifiers. Experiments of multi-object recognition on two well-known image datasets demonstrate the efficiency of the proposed system.