Decision-level Strategy Research Articles

Mask-R-FCN: A Deep Fusion Network for Semantic Segmentation

Remote sensing image classification plays a significant role in urban applications, precision agriculture, water resource management. The task of classification in the field of remote sensing is to map raw images to semantic maps. Typically, fully convolutional network (FCN) is one of the most effective deep neural networks for semantic segmentation. However, small objects in remote sensing images can be easily overlooked and misclassified as the majority label, which is often the background of the image. Although many works have attempted to deal with this problem, making a trade-off between background semantics and edge details is still a problem. This is mainly because they are based on a single neural network model. To deal with this problem, a convolutional deep network with regions (R-CNN), which is highly effective for object detection is leveraged as a complementary component in our work. A learning-based and decision-level strategy is applied to fuse both semantic maps from a semantic model and an object detection model. The proposed network is referred to as Mask-R-FCN. Experimental results on real remote sensing images from the Zurich dataset, Gaofen Image Dataset (GID), and DataFountain2017 show that the proposed network can obtain higher accuracy than single deep neural networks and other machine learning algorithms. The proposed network achieved better average accuracies, which are approximately 2% higher than those of any other single deep neural networks on the Zurich, GID, and DataFoundation2017 datasets.

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

In multi-modal emotion aware frameworks, it is essential to estimate the emotional features then fuse them to different degrees. This basically follows either a feature-level or decision-level strategy. In all likelihood, while features from several modalities may enhance the classification performance, they might exhibit high dimensionality and make the learning process complex for the most used machine learning algorithms. To overcome issues of feature extraction and multi-modal fusion, hybrid fuzzy-evolutionary computation methodologies are employed to demonstrate ultra-strong capability of learning features and dimensionality reduction. This paper proposes a novel multi-modal emotion aware system by fusing speech with EEG modalities. Firstly, a mixing feature set of speaker-dependent and independent characteristics is estimated from speech signal. Further, EEG is utilized as inner channel complementing speech for more authoritative recognition, by extracting multiple features belonging to time, frequency, and time–frequency. For classifying unimodal data of either speech or EEG, a hybrid fuzzy c-means-genetic algorithm-neural network model is proposed, where its fitness function finds the optimal fuzzy cluster number reducing the classification error. To fuse speech with EEG information, a separate classifier is used for each modality, then output is computed by integrating their posterior probabilities. Results show the superiority of the proposed model, where the overall performance in terms of accuracy average rates is 98.06%, and 97.28%, and 98.53% for EEG, speech, and multi-modal recognition, respectively. The proposed model is also applied to two public databases for speech and EEG, namely: SAVEE and MAHNOB, which achieve accuracies of 98.21% and 98.26%, respectively.