In recent years, the performance of deep neural networks in improving the image retrieval process has been remarkable. Utilizing deep neural networks; however, leads to poor results in retrieving images with missing regions. The operators’ dysfunctions, who consider the relationship between the image pixels, statistically extract incomplete information from an image, which in turn reduces the number of image features and or leads to features' inaccurate identification. An attempt has been made to eliminate the problem of missing image information through image inpainting techniques; therefore, a content-based image retrieval method is proposed for images with missing regions. In this method, through image inpainting the crucial missing information is reconstructed. The image dataset is being queried to find similar samples. For this purpose, a two-stage inpainting framework based on encoder-decoder is used in the image retrieval system. Also, the features of each image are extracted from the integration and concatenating of content and semantic features. Through using handcraft features such as color and texture image content information is extracted from the ResNet-50 deep neural network. Finally, similar images are retrieved based on the minimum Euclidean distance. The performance of the image retrieval model with missing regions is evaluated with the average precision criterion on the Paris 6K datasets. The best retrieval results are 60.11%, 50.14%, and 42.43% for retrieving the top one, five, and ten samples after reconstructing the image with the most missing regions with a destruction frequency of 6 Hz, respectively.

Improving the quality of food industries and the safety and health of the people’s nutrition system is one of the important goals of governments. Fish is an excellent source of protein. Freshness is one of the most important quality criteria for fish that should be selected for consumption. It has been shown that due to improper storage conditions of fish, bacteria, and toxins may cause diseases for human health. The conventional methods of detecting spoilage and disease in fish, i.e. analyzing fish samples in the laboratory, are laborious and time-consuming. In this paper, an automatic method for identifying spoiled fish from fresh fish is proposed. In the proposed method, images of fish eyes are used. Fresh fish are identified by shiny eyes, and poor and stale fish are identified by gray color changes in the eye. In the proposed method, Inception-ResNet-v2 convolutional neural network is used to extract features. To increase the accuracy of the model and prevent overfitting, only some useful features are selected using the mRMR feature selection method. The mRMR reduces the dimensionality of the data and improves the classification accuracy. Then, since the number of samples is low, the k-fold cross-validation method is used. Finally, for classifying the samples, Naïve bayes and Random forest classifiers are used. The proposed method has reached an accuracy of 97% on the fish eye dataset, which is better than previous references.