Image Classification with SVM for CMOS Sensor Generating a Vector Image

Abstract

In the coming Internet of Things (IoT) era, it is important to reduce the volume of data being output by sensors as well as their power consumption. Since conventional image sensor output data for photography are often redundant in AI applications, image sensors that can output lightweight data for use in AI are needed. In this work, we propose a complementary metal oxide semiconductor (CMOS) image sensor (CIS) pixel circuit that can extract intensity gradients without the use of analog memories. The gradients are available for histogram of oriented gradients (HoG) features that can reduce the amount of data used in image classification. We performed experiments to evaluate whether the HoG features calculated by the outputs from our image sensor pixels were suitable for image classification tasks. A support vector machine (SVM) classifier was trained with simulated sensor outputs to evaluate human detection accuracy. We also evaluated the accuracy when the sensor outputs were quantized using "low bit decimation" and "value clipping" to reduce the amount of data. Our experimental results indicated that the highest accuracy of 99.55% was achieved using the 2-bit-width quantized gradients by value clipping and HoG features calculated with 4 × 4 cells.