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RSILC: Rotation- and Scale-Invariant, Line-based Color-aware descriptor.
Modern appearance-based object recognition systems typically involve feature/descriptor extraction and matching stages. The extracted descriptors are expected to be robust to illumination changes and to reasonable (rigid or affine) image/object transformations. Some descriptors work well for general object matching, but gray-scale key-point-based methods may be suboptimal for matching line-rich color scenes/objects such as cars, buildings, and faces. We present a rotation- and scale-invariant, line-based color-aware descriptor (RSILC), which allows matching of objects/scenes in terms of their key-lines, line-region properties, and line spatial arrangements. An important special application is face matching, since face characteristics are best captured by lines/curves. We tested RSILC performance on publicly available datasets, and compared it with other descriptors. Our experiments show that RSILC is more accurate in line-rich object description than other well-known generic object descriptors.