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The current state of the research in landmark recognition highlights the goodaccuracy which can be achieved by embedding techniques, such as Fisher vectorand VLAD. All these techniques do not exploit spatial information, i.e.consider all the features and the corresponding descriptors without embeddingtheir location in the image. This paper presents a new variant of thewell-known VLAD (Vector of Locally Aggregated Descriptors) embedding techniquewhich accounts, at a certain degree, for the location of features. The drivingmotivation comes from the observation that, usually, the most interesting partof an image (e.g., the landmark to be recognized) is almost at the center ofthe image, while the features at the borders are irrelevant features which dono depend on the landmark. The proposed variant, called locVLAD (location-awareVLAD), computes the mean of the two global descriptors: the VLAD executed onthe entire original image, and the one computed on a cropped image whichremoves a certain percentage of the image borders. This simple variant shows anaccuracy greater than the existing state-of-the-art approach. Experiments areconducted on two public datasets (ZuBuD and Holidays) which are used both fortraining and testing. Morever a more balanced version of ZuBuD is proposed.

A location-aware embedding technique for accurate landmark recognition