Integration of global and local salient features for scene modeling in mobile robot applications

Many approaches have recently used global image descriptors and/or local key-point descriptors for scene understanding. In fact these approaches have suffered from lack of spatial information by using local key-point descriptors, and lack of viewpoint and local information by using global image descriptors. To overcome these problems, this paper addresses a novel image descriptor based on salient line segments (SLS), in which the global and local image features are integrated into low dimensional feature vectors. In this descriptor, low level feature maps are first computed in four scales by applying a center-surround competition technique to enhance the dominant edges and suppress small line segments. These maps are then used to extract the SLS of the image patches by creating histogram of gradients in the receptive cells. Afterwards, the global features are formed into a single vector from the coarser scales of the SLSs, and the local feature vectors are formed from the frequency of the appearance of SLSs in the finer scale. Finally, a classification step recognizes the scene of an input image by applying multi-class SVM with a Radial Bias Function (RBF) kernel. The system is performed on image sequences taken from natural scenes by a mobile agent under controlled and unexpected changes in environmental conditions. Experiments on image datasets show that the proposed method is able to classify the scenes more accurately than former methods in mobile agent environments.

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