Image local feature descriptors are widely used in various classification and matching scenarios. Traditional local descriptors only extract features at specific scale. In order to improve the performance, some methods extract multi-scale and multi-angular features and construct descriptors by generalization, accumulation or ranking. However, the information of descriptors on bins and scales is not fully utilized, so there is a certain improvement space. To solve this problem, a method of constructing local descriptor based on two directions reconstruction transformation is proposed. Firstly, the stability of bins under different scale pairs is calculated, and then the stability of each bin relative to other bins is calculated. Then the cumulative gradient migration in scales direction is calculated, and the relative stability is combined into the score of a scale pair. The sum of the scores of all scale pairs of a bin constitutes the total score of the bin and eventually forms the vector of the descriptor. Experiments on two general datasets show that the accuracy of the proposed descriptor is improved without expanding the dimension.
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