Role of the secondary visual cortex in HMAX model for object recognition

The models inspired by visual systems of life creatures (e.g., human, mammals, etc.) have been very successful in addressing object recognition tasks. For example, Hierarchical Model And X (HMAX) effectively recognizes different objects by modeling the V1, V4, and IT regions of the human visual system. Although HMAX is one of the superior models in the field of object recognition, its implementation has been limited due to some disadvantages such as the unrepeatability of the process under constant conditions, extreme redundancy, high computational load, and time-consuming. In this paper, we aim at revising the HMAX approach by adding the model of the secondary region (V2) in the human visual system which leads to removing the mentioned drawbacks of standard HMAX. The added layer selects repeatable and more informative features that increase the accuracy of the proposed method by avoiding the redundancy existing in the conventional approaches. Furthermore, this feature selection strategy considerably reduces the huge computational load. Another contribution of our model is highlighted when a small number of training images is available where our model can efficiently cope with this issue. We evaluate our proposed approach using Caltech5 and GRAZ-02 database as two famous benchmarks for object recognition tasks. Additionally, the results are compared with standard HMAX that validate and highlight the efficiency of the proposed method.