Deep supervised hashing has turned up to unravel many large-scale image retrieval challenges. Although deep supervised hashing accomplishes good results for image retrieval process, requisite for further improving the retrieval accuracy always remains the primal focus of interest. In Deep hashing methods, feature representation happens at the outset of the fully connected (FC) layers, causing shortage of spatial information owing to its global nature, whereas deeper pooling layers preserve semantically similar information by retaining the images spatial information, which can result in uplifting the retrieval performance. Hereby, for enhancing the image retrieval accuracy through exploring spatial information, a novel way of deep supervised hashing based on Pooled Feature map (DSHPoolF) is proposed to generate compact hash codes that explore the spatial information by weighing the informative Feature maps from the last pooling layer. This is achieved, firstly, by weighing the last pooling layers Feature map in two ways, namely average–max-based pooling and probability-based pooling strategies. Secondly, informative Feature maps are selected with the help of the weights. In addition to this, the informative Feature maps play a key role in optimizing quantization error together with the loss function and classification errors in a single-step, point-wise ranking manner. This proposed DSHPoolF method is assessed using three datasets (CIFAR-10, MNIST and ImageNet) that unveils primitive outcome in comparison with other existing prominent hash-based methods.

中文翻译：

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DSHPoolF：基于选择性池特征图的深度监督哈希图像检索

深度监督哈希已经解决了许多大规模图像检索挑战。尽管深度监督哈希在图像检索过程中取得了良好的效果，但进一步提高检索精度的必要条件始终是人们关注的主要焦点。在深度散列方法中，特征表示发生在全连接 (FC) 层的开始，由于其全局性质导致空间信息短缺，而更深的池化层通过保留图像空间信息来保留语义相似的信息，这可能导致提升检索性能。因此，为了通过探索空间信息来提高图像检索的准确性，提出了一种基于池化特征图 (DSHPoolF) 的深度监督散列的新方法，以生成紧凑的散列码，通过权衡来自最后一个池化层的信息特征图来探索空间信息。这是通过两种方式对最后一个池化层的特征图进行加权来实现的，即基于平均最大的池化和基于概率的池化策略。其次，在权重的帮助下选择信息特征图。除此之外，信息丰富的特征图在以单步、逐点排序的方式优化量化误差以及损失函数和分类误差方面发挥着关键作用。这种提议的 DSHPoolF 方法使用三个数据集（CIFAR-10、MNIST 和 ImageNet）进行评估，与其他现有的突出的基于哈希的方法相比，这些数据集揭示了原始结果。