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Filtering Deep Convolutional Features for Image Retrieval

In image retrieval, highlighting target object and reducing the influence of background noise remains challenging. To address this problem, we propose a novel weighting method that aggregates deep convolutional features based on filtering, called filtering on spatial channel weighting (FSCW) factors, to represent image contents, and utilize it for image retrieval. There are three main contributions of this study. First, the designed filter can effectively remove the influence of background noise. Second, we propose a new channel selection and spatial weighting method, which can accurately distinguish target object from the background noise. Finally, we designed a new channel weighting strategy to suppress intra-image visual burstiness. Experimental results on benchmark datasets demonstrate that the proposed method effectively enhances discriminative power and outperforms some existing state-of-the-art methods in terms of the mAP metric. Furthermore, the proposed method is superior to some existing algorithms in distinguishing background noise and target object.