Biologically-inspired object recognition system for recognizing natural scene categories

Visual processing has attracted a lot of attention in the last decade. Hierarchical approaches for object recognition are gradually becoming widely-accepted. Generally, they are inspired by the ventral stream of human visual cortex, which is in charge of rapid categorization. Similar to objects, natural scenes share common features and can, therefore, be classified in the same manner. However, natural scenes generally show a high level of statistical correlation between classes. This, in fact, is a major challenge for most object recognition models. Rapid categorization of a natural scene in the absence of attention is a challenge. However, researchers have found that 150 ms is enough to categorize a complex natural scene. We tested the capability of our recent and bio-inspired En-HMAX model of visual processing for scene classification. The results show the En-HMAX model has a comparable performance to state of the art methods for natural scene categorization.