Classifying the visual features in images to retrieve a specific image is a significant problem within the computer vision field especially when dealing with historical faded colored images. Thus, there were lots of efforts trying to automate the classification operation and retrieve similar images accurately. To reach this goal, we developed a VGG19 deep convolutional neural network to extract the visual features from the images automatically. Then, the distances among the extracted features vectors are measured and a similarity score is generated using a Siamese deep neural network. The Siamese model built and trained at first from scratch but, it didn't generated high evaluation metrices. Thus, we re-built it from VGG19 pre-trained deep learning model to generate higher evaluation metrices. Afterward, three different distance metrics combined with the Sigmoid activation function are experimented looking for the most accurate method for measuring the similarities among the retrieved images. Reaching that the highest evaluation parameters generated using the Cosine distance metric. Moreover, the Graphics Processing Unit (GPU) utilized to run the code instead of running it on the Central Processing Unit (CPU). This step optimized the execution further since it expedited both the training and the retrieval time efficiently. After extensive experimentation, we reached satisfactory solution recording 0.98 and 0.99 F-score for the classification and for the retrieval, respectively. |
