Local Feature Detectors, Descriptors, and Image Representations: A Survey

Abstract

With the advances in both stable interest region detectors and robust and distinctive descriptors, local featurebased image or object retrieval has become a popular research topic. The other key technology for image retrieval systems is image representation such as the bag-of-visual words (BoVW), Fisher vector, or Vector of Locally Aggregated Descriptors (VLAD) framework. In this paper, we review local features and image representations for image retrieval. Because many and many methods are proposed in this area, these methods are grouped into several classes and summarized. In addition, recent deep learning-based approaches for image retrieval are briefly reviewed.

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“…Feature-based detectors and descriptors are widely used, due to their speed in computing the salient features of images. For increased robustness in object detection, these features should be invariant to rotation, scale and affine transformations over several frames [29]. To find correspondences between two images, we consider a set of features in the template image F T ∈ R n and the current frame F S ∈ R m , where n, m ∈ Z represent the number of features in each image.…”