BIOLOGICALLY-INSPIRED OBJECT RECOGNITION SYSTEM WITH FEATURES FROM COMPLEX WAVELETS

In this paper, a novel cortex-inspired feed-forward hierarchical ob-ject recognition system based on complex wavelets is proposed and tested. Complex wavelets contain three key properties for object representation: shift invariance, which enables the extraction of sta-ble local features; good directional selectivity, which simplifies the determination of image orientations; and limited redundancy, which allows for efficient signal analysis using the multi-resolution decom-position offered by complex wavelets. In this paper, we propose a complete cortex-inspired object recognition system based on com-plex wavelets. We find that the implementation of the HMAX model for object recognition in [1, 2] is rather over-complete and includes too much redundant information and processing. We have optimized the structure of the model to make it more efficient. Specifically, we have used the Caltech5 standard dataset to compare with Serre’s model in [2] (which employs Gabor filter bands). Results demon-strate that the complex wavelet model achieves a speed improvement of about 4 times over the Serre model and gives comparable recog-nition performance