MiSPOT: dynamic product placement for digital TV through MPEG-4 processing and semantic reasoning
Knowledge and Information Systems
Semi-supervised local discriminant embedding
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
| Hi-index | 0.00 |
Edge Directions Histograms are widely used as an image descriptor for image retrieval and recognition applications. Edges represent textures and are also representative of the image shapes. In this work a histogram of the edge pixel directions is defined for image description. The edges de- tected with the Canny algorithm will be described in two different scales in four directions. In the lower scale the image is divided into 16 sub-images, and a descriptor with 64 bins results. In the higher scale, as no image division is done because only the most important image features will be present, 4 bins result. A total of 68 bins are used to describe the image in scale-space. Images will be compared using the Euclidean distance between histograms. The provided results will be compared with the ones that result from the use of the histogram in the low scale only. Improved classifi- cation using the Nearest Class Mean and Neural Networks will be used. A higher level semantic annotation, based on this low level descriptor that results from the multiscale im- age analysis, will be extracted. Keywords: Image Description, Edge Description, His- togram, Image Classification, Scale-space.