Image Descriptors Based on the Edge Orientation

Edges are one of the most important image visual features. They are highly related with shapes and can also be representative of the image textures. Edge orientation histograms are usually very reliable descriptors suitable for image analysis, search and retrieval. In this work two methods to compute edge based orientation descriptors are reported: the "Edge Pixel Orientations Histogram" and the "Angular Orientation Partition Edge Descriptor". Edges are detected with Canny algorithm. The resulting edge pixels are separated into N
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gradient orientation intervals. For the first descriptor, edges detected without and with hysteresis thresholding, result in a histogram of gradient orientations. The two edge images are divided into N × N sub-images, resulting in a 2N
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N
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bins histogram. In the second descriptor, after an angular division of the image, edges are described by their angular orientations. Considering N
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angular divisions, and N
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angular orientations, a descriptor with N
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N
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bins results. Because of the angular geometry, this descriptor is resilient to rotation and by shifting the center of the angular division it is also possible to add translation resilience. Two examples of automatic image semantic annotation using this description method is reported using a database with 738 keyframes and the JPSearch database with 971 high resolution images (3888 × 2592). The K Nearest Neighbor is used as classifier and the Manhattan distance is used for image similarity computation. The two descriptors annotation performance are compared between them, with the MPEG-7 Edge Histogram Descriptor and with the SIFT descriptor.