Image Representation Optimization Based on Locally Aggregated Descriptors
Aggregating local descriptors into super vectors achives excellent performance in image classification and retrieval tasks. Vector of locally aggregated descriptorsVLAD, which indexes images to compact representations by aggregating the residuals of descriptors and visual words, is a popular super vector encoding method among this kind. This paper will focus on the biggest difficulty of VLAD, the "visual burstiness" reviste the basic assumptions and solutions along this line, then make modifications to two key steps of the initial VLAD process. The main contributions are twofold. Firstly, we start from local coordinate systemLCS and propose the aggregated versionaggrLCS, which changes the objective and timing of coordinate rotation, for better captures of bursts. Secondly, an adaptive power law normalization method is adopted to magnify the positive effect of power law normalization by weighting each dimension respectively. Experiments on image retrieval tasks demonstrate that the proposed modifications show superior performance over the original and several variants of VLAD.
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