To address the lack of explicit concepts and effective methods with learning in most hierarchical visual computational models

we propose a novel hierarchical generic object recognition sketch using neural sparse coding. Firstly

the learning strategies are embedded in a hierarchical system for generating several prototypes to model the characteristic of complex cell receptive fields. Secondly

based on a hierarchical sparse coding process

we present a hierarchical feature extraction method for generic object recognition. Finally

a simplified classifier is designed to achieve our goals in complex scenes according to the extracted robust features. Experiments on Caltech-101 demonstrate the effectiveness in our method and the more preferable results comparing with Serre’s show greater consistency in biological vision.