## 4 Experimental Results
### Results and Discussion on Noise-Induced Hybrid Dataset Using RIS-DUGL  
A comprehensive noise study has also been carried out to check the proposed reverse image search system's robustness. Three types of noise, namely, Salt & Pepper, Speckle, and Gaussian-noise, have been used to impregnate the original dataset. Salt & Pepper noise is an artifact that has a dark intensity value in bright areas and bright intensity values in dark regions. This type of noise can be triggered by analog-to-digital conversion. Gaussian  
Figure 13: Results on WANG 1000 images.  
noise is nothing but a random valued noise that follows a uniform or other distribution. It can have any random value of random variables [48].  
A sample image with noisy ones has been shown in Figure 16. In this work, noise is added with a 0.05 ratio to corrupt the original dataset. Retrieval accuracy for noisy data implementation has been found to be 97%, 96%, and 97% using Gaussian, Salt & Pepper, and Speckle noise, respectively. This clearly indicates the effectiveness and high capability of the proposed method in the case of using dynamic feature extraction. Hence, we can announce that dynamic feature extraction (using Deep Learning) proves to have more learning ability and focuses more on generalization. Figure 17 summarizes the experimental results when the RIS-DUGL dataset is trained and tested on noisy images, starting with the speckle noise in the first query and retrieved images followed by Gaussian and Salt & Pepper noise. For noisy image retrieval, starting from the first sample image of a human with a face-up position, which includes Speckle noise, the proposed method retrieves the exact human with its standing position. This clearly indicates the importance and high capability of this work for criminal detection. In the case of the horse sample image, the image retrieved has the best match of the brown horse having a quite different pose and a distant object in the sample image. Clearly, when tested with the flower sample image containing Salt & Pepper noise, the proposed RIS-DUGL technique returned its image to the same flower class with many instances, and the manipulated color is also identified. In the case of a rotationally transformed cap, a sample image containing Gaussian noise, the similar orientation-wise transformed image is retrieved as the best image. The optimal training performance of RIS-DUGL is attained by using a maximum of 25 iterations for two layers of deep SAE, as can be seen from Figure 18, at the 25 th iteration, the learning performance gets saturated.  
Figure 14: Results on IAS-RGB Lab dataset.  
Figure 16: Sample images corrupted with three types of noise.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methodologies**} & **Retrieval Accuracy** & **Average Feature** & **Average Retrieval Time** \\  & **(Precision)** & **Extraction Time per Image** & **per Image** \\  & & **(s)** & **(s)** \\ \hline CBIR with SVM based & 39.24\% & 5.23 & 6.79 \\ Classification [14] & 74.42\% & 0.31 & 0.32 \\ \hline Similarity Evaluation & 94.56\% & 0.23 & 0.28 \\ (AlexNet) & 98.46\% & 0.50 & 0.23 \\ \hline Proposed RIS-DUGL & 98.46\% & 0.50 & 0.23 \\ (VGG-16) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of time and accuracy of the proposed model with conventional methods.  
Figure 15: Results on Merchant dataset.  
Figure 17: Results of RIS-DUCL using noisy hybrid dataset.  
Figure 18: Training process of Sparse Auto-encoder (SAE) in the proposed.