Context-Aware Discriminative Vocabulary Learning for Mobile Landmark Recognition

This paper proposes a discriminative vocabulary learning for landmark recognition based on the context information acquired from mobile devices. The vocabulary learning generates a set of discriminative codewords for image representation, which is important for landmark recognition. Many state-of-the-art methods use content analysis alone for vocabulary learning, which underutilizes the context information provided by mobile devices, such as location from the GPS positioner and direction from the digital compass. Although some works start to consider the images' location information for vocabulary learning, the location alone is insufficient since GPS data has significant errors in dense built-up areas. The context analysis techniques that use GPS to shortlist the geographically nearby landmark candidates for subsequent image matching are at times inadequate. In view of this, the paper proposes to employ both direction and location information to learn a discriminative compact vocabulary (DCV) for mobile landmark recognition. Direction information is first considered to supervise image feature clustering to construct direction-dependent scalable vocabulary trees (DSVTs). Location information is then incorporated into the proposed DCV learning algorithm, to select the discriminative codewords of the DSVT to form the DCV. An ImageRank technique and an iterative codeword selection algorithm are developed for DCV learning. Experimental results using the NTU50Landmark database show that the proposed approach achieves 4% improvement over the current method in mobile landmark recognition.