## 3 Experimental Setting and Results
### Experimental Results
#### 3.3.2 Quantitative Analysis  
In this section, we adopt _recall_ curves and _mAP_ to quantitatively analyze the ANN search performance of the proposed TOCEH and the other seven state-of-the-art hashing methods. These hashing methods separately generate 64-, 128-, and 256-bit binary code to represent the image content. The _mAP_ values are in Tables 3-5. The recall curves are shown in Figures 10-12. Figure 11: The recall curves of all comparative methods on SAT-4 and the data are separately encoded as (**a**) 64-, (**b**) 128- and (**c**) 256-bit binary code. Figure 10: The recall curves of all comparative methods on UCMD; the data are separately encoded as (**a**) 64-, (**b**) 128- and (**c**) 256-bit binary code. From the quantitative results, we know TOCEH achieves the best ANN search performance. LSH [14], the data-independent hashing algorithm, randomly generates hashing projection functions without a training process. As a result, the ANN search performance of LSH cannot drastically improve as the number of binary bits increases [9]. In contrast, the proposed TOCEH and the other nine comparative hashing methods utilize a machine learning mechanism to obtain the hashing functions, which are adaptive to the training data distribution. Thus, these machine-learning-based hashing algorithms achieve a better ANN search performance than LSH. SH [17] establishes a spectral graph to measure the similarity relation among samples, and divides the samples into different cluster groups by spectral graph partition. Then, SH [17] assigns the same code to the samples in the same group. For a large-scale RS image dataset, the time complexity of establishing a spectral graph would be high. Both ITQ [13] and KMH [12] first learn encoding centers, then assign the samples as the same binary code as their nearest center. ITQ [13] considers the fixed vertexes of a hyper cubic as centers, but they are not well adapted to the training data distribution. KMH [12] learns the encoding centers with minimal quantization loss and similarity loss by a k-means iterative mechanism. This measure effectively helps KMH improve the ANN search performance. To balance the training complexity and ANN search performance, PRH [23] employs the partial randomness and partial learning strategy to generate hashing functions. LSH [14], SH [17], ITQ [13], KMH [12] and PRH [23] belong to the shallow hashing algorithms, and their performances relate to the quality of the intermediate high dimensional features. To eliminate this effect, TOCEH, TBH [10], DVB [39], DH [40], DeepBit [41] and DCH [11] adopt a deep learning framework to learn the end-to-end binary feature, which can further boost the ANN search performance. The classical DH [40] proposes three constraints at the top layer of the deep network: the quantization loss, balance bits and independent bits. However, the pair-wise similarity preserving or the triplet ordinal relation preserving is not considered in DH. This may lead a poor performance of DH. The same problem also exists in DeepBit [41]. However, DeepBit  
Figure 12: The recall curves of all comparative methods on SAT-6 and the data are separately encoded as (a) 64-, (b) 128- and (c) 256-bit binary code. augments the training data with different rotations and further updates the parameters of the network. This measure helps DeepBit to obtain a better ANN search performance than DH. For most deep hashing, it is hard to unveil the intrinsic structure of the whole sample space by simply regularizing the output codes within each single training batch. In contrast, the conditional auto-encoding variational Bayesian networks are introduced in DVB to exploit the feature space structure of the training data using the latent variables. DCH [11] pre-trains a similarity graph and expects that the probability distribution in the Hamming space should be consistent with that in the Euclidean space. TBH [10] abandons the process of the pre-computing similarity graph and embeds it in the deep neural network.