DCI: Discriminative And Contrast Invertible Descriptor
2018 Β· Zhenwei Miao, Kim-Hui Yap, Xudong Jiang, et al.
Abstract
Local feature descriptors have been widely used in fine-grained visual object search thanks to their robustness in scale and rotation variation and cluttered background. However, the performance of such descriptors drops under severe illumination changes. In this paper, we proposed a Discriminative and Contrast Invertible (DCI) local feature descriptor. In order to increase the discriminative ability of the descriptor under illumination changes, a Laplace gradient based histogram is proposed. A robust contrast flipping estimate is proposed based on the divergence of a local region. Experiments on fine-grained object recognition and retrieval applications demonstrate the superior performance of DCI descriptor to others.
Authors
(none)
Tags
Stats
Related papers
- Multichannel Distributed Local Pattern For Content Based Indexing And Retrieval (2018)3.58
- If-net: An Illumination-invariant Feature Network (2020)6.77
- Local Gradient Hexa Pattern: A Descriptor For Face Recognition And Retrieval (2022)13.93
- LDOP: Local Directional Order Pattern For Robust Face Retrieval (2018)11.29
- Local Neighborhood Intensity Pattern: A New Texture Feature Descriptor For Image Retrieval (2017)14.11
- Deeppoint3d: Learning Discriminative Local Descriptors Using Deep Metric Learning On 3D Point Clouds (2019)9.59
- Face Retrieval Using Frequency Decoded Local Descriptor (2017)10.61
- Selective Convolutional Descriptor Aggregation For Fine-grained Image Retrieval (2016)19.37