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A GOMM is constructed in three steps. First, the gradient orientation of each pixel is mapped onto N intervals and the gradient modulus is partitioned into M levels. Next, a block is constructed from the gradient modulus of pixels whose gradient orientations are mapped onto the same interval. Then, each component of the GOMM is given by the sum of the ratios between two terms, namely, the differential gradient modulus grading between the elements in the above-mentioned block, and the distance between the elements. Finally, a six-dimension vector is calculated from each GOMM. By rearranging feature vectors from each GOMM, we can concatenate the vectors to construct a uniform GOMM feature for a given image, irrespective of the angle of the image. Experimental results on the KTH-TIPS2 (The Royal Institute of Technology - Textures under varying Illumination, Pose and Scale) image database show that the GOMM significantly outperforms the other classical descriptors. |
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Image classification
Databases
Image analysis
Image segmentation
Human vision and color perception
Facial recognition systems
Image processing
