Integrated VGG19 and Gated Recurrent Unit with Cosine Similarity Metric for Content based Image Retrieval

Technological advancement has provided huge databases for different genres of image to obtain efficient visual information to satisfy users. The existing algorithms tried to extract the important feature vector from the images to analyze the similarities between the query image and dataset image but failed to reach their expected results. The noises generated during model training, overfitting and gradient vanishing problems are the problems faced by the existing models. To overcome the problems of the existing methods, the VGG19 and Gated Recurrent Unit (GRU) with cosine similarity are proposed for content-based image retrieval. The z-score normalization technique was used to standardize the intensity of the images which enhanced the training of the model. The VGG19 algorithm extracted spatial features and the GRU model extracted the temporal features from both database and query images. The cosine similarity metric was used for calculating the similarities of both the images and retrieved based on the feature vector similarities. The proposed VGG19 and GRU with cosine similarity metric has obtained a better average precision of 98.89% for Corel 1 K dataset and 84.90% for Corel 10 K dataset compared to the existing Hybrid Graph-based Gray Level Co-Occurrence Matrix (HGGLCM) model.