H.6.5.13. Signal processing
Zeynab Mohammadpoory; Mahda Nasrollahzadeh; Sakineh Asadi
Abstract
Nowadays, the recognition of emotions using speech signals has gained popularity because of its vast number of applications in different fields such as medicine, online marketing, online search engines, education systems, criminal investigations, traffic collisions, and more. Many researchers have adopted ...
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Nowadays, the recognition of emotions using speech signals has gained popularity because of its vast number of applications in different fields such as medicine, online marketing, online search engines, education systems, criminal investigations, traffic collisions, and more. Many researchers have adopted different methodologies to improve emotion classification accuracy using speech signals. This study presents a novel time-series-to-graph transformation framework for speech emotion recognition. Speech signals were segmented into overlapping windows, each converted into graphs, from which 16 structural features were extracted. Significant features were then selected via Minimum Redundancy Maximum Relevance (mRMR) and used to train four classifiers: random forest (RF), linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbors (KNN). Finally, a soft-voting ensemble strategy was employed to integrate their predictions, yielding improved classification performance. The proposed method achieved the highest sensitivity, specificity, and accuracy for the SAVEE database: 83.57%, 98.93%, and 98.16%, respectively. Similarly, for the EmoDB database, the highest values were 94.47%, 99.09%, and 98.40%, respectively. We also compared our results with other methods and found that our method outperformed state-of-the-art techniques in emotion classification.
H.5. Image Processing and Computer Vision
Reza Kharghanian; Zeynab Mohammadpoory
Abstract
The Convolutional Restricted Boltzmann Machine (CRBM) is a generative model that extracts representations from unlabeled data, achieving success in various applications. However, its unsupervised nature may yield suboptimal representations for specific classification tasks. This paper proposes adapting ...
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The Convolutional Restricted Boltzmann Machine (CRBM) is a generative model that extracts representations from unlabeled data, achieving success in various applications. However, its unsupervised nature may yield suboptimal representations for specific classification tasks. This paper proposes adapting k-means clustering to enhance CRBM parameters, aligning features with informative cluster centers. A novel criterion combining generative and soft-K-Means objectives optimizes both cluster centers and CRBM parameters, allowing for continued unsupervised feature learning.Experiments on MNIST, CIFAR10, and three facial expression datasets (JAFFE, KANADE, BU) show that the proposed method enhances the learning process and offers a more informative representation compared to standard and classification CRBM.
H.5. Image Processing and Computer Vision
Sekine Asadi Amiri; Mahda Nasrolahzadeh; Zeynab Mohammadpoory; AbdolAli Movahedinia; Amirhossein Zare
Abstract
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 ...
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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.
H.5. Image Processing and Computer Vision
S. Asadi Amiri; Z. Mohammadpoory; M. Nasrolahzadeh
Abstract
Content based image retrieval (CBIR) systems compare a query image with images in a dataset to find similar images to a query image. In this paper a novel and efficient CBIR system is proposed using color and texture features. The color features are represented by color moments and color histograms of ...
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Content based image retrieval (CBIR) systems compare a query image with images in a dataset to find similar images to a query image. In this paper a novel and efficient CBIR system is proposed using color and texture features. The color features are represented by color moments and color histograms of RGB and HSV color spaces and texture features are represented by localized Discrete Cosine Transform (DCT) and localized Gray level co-occurrence matrix and local binary patterns (LBP). The DCT coefficients and Gray level co-occurrence matrix of the blocks are examined for assessing the block details. Also, LBP is used for rotation invariant texture information of the image. After feature extraction, Shannon entropy criterion is used to reduce inefficient features. Finally, an improved version of Canberra distance is employed to compare similarity of feature vectors. Experimental analysis is carried out using precision and recall on Corel-5K and Corel-10K datasets. Results demonstrate that the proposed method can efficiently improve the precision and recall and outperforms the most existing methods.s the most existing methods.
