Bird Species Classification Based on Color Features

This paper presents a novel approach for bird species classification based on color features extracted from unconstrained images. This means that the birds may appear in different scenarios as well may present different poses, sizes and angles of view. Besides, the images present strong variations in illuminations and parts of the birds may be occluded by other elements of the scenario. The proposed approach first applies a color segmentation algorithm in an attempt to eliminate background elements and to delimit candidate regions where the bird may be present within the image. Next, the image is split into component planes and from each plane, normalized color histograms are computed from these candidate regions. After aggregation processing is employed to reduce the number of the intervals of the histograms to a fixed number of bins. The histogram bins are used as feature vectors to by a learning algorithm to try to distinguish between the different numbers of bird species. Experimental results on the CUB-200 dataset show that the segmentation algorithm achieves 75% of correct segmentation rate. Furthermore, the bird species classification rate varies between 90% and 8%, depending on the number of classes taken into account.