Logo Recognition via Fusion of Spatial and Spectral Features

Machine vision based logo and trademark recognition, is one of the most efficient and widely used method to measure brand awareness on internet and social media. Similarity of logos geometric structure, difference pose and lighting conditions are the leading factors that makes the recognition task tedious. For this reason, different image descriptors have been used to extract the same information under various conditions. In this work, we examine fusion of image descriptors which obtained by extracting data from spectral and spatial domains independently. thereby features extracted from various domains targeted to form non-overlapping distinctive feature vectors. As spectral and spatial features we used GIST and FHOG descriptors. Experimental results held on the latest dataset Logos-32plus. Quantitative evaluation shows that our method have higher accuracy rates against the state of the art method.