SCIENTIA SINICA Informationis, Volume 47 , Issue 8 : 1109-1(2017) https://doi.org/10.1360/N112016-00307

## Large-scale image retrieval based on multi-feature and multi-kernel hashing learning

• AcceptedAug 1, 2017
• PublishedAug 23, 2017
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### References

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• Figure 1

(Color online) The diagram of multi-feature and multi-kernel hashing learning

• Figure 2

The structure of multi-feature and multi-kernel hashing learning

• Figure 3

(Color online) Error vs. iterations on (a) IRMA dataset, (b) Ultrasound dataset, and (c) Cifar10 dataset

• Figure 4

(Color online) The relationship based on kernel hashing methods between thelength of the hash code and the accuracy rate when thenumber of retrieved samples is 50

• Figure 5

(Color online) The relationship based on kernel hashing methods between the length of the hash code and the recall rate when the number of retrieved samples is 50

• Figure 6

(Color online) The relationship between thelength of the hash code and the accuracy rate based on single feature and single kernel hashing methods of M2FM3KH when thenumber of retrieved samples is 50

• Figure 7

(Color online) The relationship between thelength of the hash code and the recall rate based on single feature and single kernel hashing methods of M2FM3KH when thenumber of retrieved samples is 50

•

Algorithm 1 多特征多核哈希检索算法

训练过程:

Require:训练集$X = \{ ({x_1},{y_1}),({x_2},{y_2}),\ldots ,({x_N},{y_N})\}$;

Output:$W$, $b$, $\mu$, $\alpha$;

1. ${\mu _j} = \frac{1}{{{M_1}}}, j = 1,\ldots ,{M_1}$和${\alpha _i} = \frac{1}{{{M_2}}}, i = 1,\ldots ,{M_2}$;

repeat

2. 固定$\alpha$和$\mu$, 求解$W$, $b$通过式(sect. 3.2);

3. 固定$W$, $b$和$\alpha$, 求解$\mu$通过式(sect. 3.2.2);

4. 固定$W$, $b$和$\mu$, 求解$\alpha$通过式(sect. 3.2.3);

until 收敛.

测试过程:

Require:测试集${X_{\rm test}}$以及根据训练过程得到的$W$, $b$, $\alpha$, $\mu;$

Output:前$n$幅与待检索样本相似的图像;

1. 根据式(sect. 3.2.4)算出测试集的哈希码${Y_{\rm test}}$;

2. 计算测试集哈希码${Y_{\rm test}}$与数据库中所有的图像的哈希码的汉明距离向量$P$;

3. 对汉明距离向量$P$计算出的距离进行升序排序, 返回前$n$张图像.

• Table 1   The distribution of IRMA and Ultrasound images
 IRMA Ultrasound Class Sum Training Testing Class Sum Training Testing 0 336 286 50 0 (Gallstone) 521 371 150 1 215 165 50 1 (Gallbladder polyps) 238 178 60 2 225 175 50 2 (normal Gallbladder ) 154 104 50 3 576 476 100 3 (Hydronephrosis) 386 286 100 4 217 167 50 4 (Kidney stones) 219 169 50 5 205 155 50 5 (Renal cyst) 197 147 50 6 194 144 50 6 (Normal kidneys) 303 233 70 7 284 234 50 7 (Liver cysts) 113 83 30 8 228 178 50 8 (Fatty liver) 360 280 80 9 193 143 50 9 (Normal liver) 191 131 60
• Table 2   Comparison of IRMA and Ultrasound (MAP)
 Method IRMA (MAP) Ultrasound (MAP) 8 bits 12 bits 24 bits 32 bits 48 bits 8 bits 12 bits 24 bits 32 bits 48 bits MFKH 0.620 0.680 0.640 0.670 0.582 0.109 0.121 0.137 0.163 0.155 SH 0.214 0.315 0.323 0.416 0.409 0.063 0.061 0.054 0.052 0.056 SKLSH 0.128 0.126 0.121 0.122 0.147 0.174 0.203 0.231 0.283 0.206 KLSH 0.143 0.154 0.156 0.151 0.147 0.152 0.147 0.142 0.137 0.149 M2FM3KH 0.520 0.643 0.713 0.723 0.640 0.177 0.152 0.163 0.194 0.287 DPSH 0.896 0.969 0.971 0.968 0.971 0.524 0.527 0.582 0.590 0.601
• Table 3   Comparison of Cifar10 (MAP)
 Method IRMA (MAP) 8 bits 12 bits 24 bits 32 bits 48 bits MFKH 0.235 0.261 0.252 0.241 0.247 SH 0.104 0.116 0.145 0.142 0.139 SKLSH 0.116 0.123 0.131 0.225 0.321 KLSH 0.132 0.143 0.147 0.145 0.154 M2FM3KH 0.231 0.264 0.367 0.423 0.243 DPSH 0.681 0.713 0.727 0.744 0.757

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