An Adaptive Descriptor Design for Object Recognition in the Wild

TL;DR

This paper introduces an adaptive object recognition method that remains robust across various image styles and post-processing effects by leveraging kernel view of gradient descriptors and multiple kernel learning, without needing style estimation.

Contribution

It is the first to study picture style influence on object recognition and proposes a style-invariant adaptive descriptor approach using multiple kernel learning.

Findings

Improves recognition accuracy across diverse image styles.

Effective on datasets with various photo effects.

Outperforms standard descriptors in experiments.

Abstract

Digital images nowadays have various styles of appearance, in the aspects of color tones, contrast, vignetting, and etc. These 'picture styles' are directly related to the scene radiance, image pipeline of the camera, and post processing functions. Due to the complexity and nonlinearity of these causes, popular gradient-based image descriptors won't be invariant to different picture styles, which will decline the performance of object recognition. Given that images shared online or created by individual users are taken with a wide range of devices and may be processed by various post processing functions, to find a robust object recognition system is useful and challenging. In this paper, we present the first study on the influence of picture styles for object recognition, and propose an adaptive approach based on the kernel view of gradient descriptors and multiple kernel learning, without estimating or specifying the styles of images used in training and testing. We conduct experiments on Domain Adaptation data set and Oxford Flower data set. The experiments also include several variants of the flower data set by processing the images with popular photo effects. The results demonstrate that our proposed method improve from standard descriptors in all cases.