Content Based Image Retrieval (CBIR) systems play a crucial role in efficiently organizing large image datasets for efficient image retrieval based on visual content. In this work, we focus on Natural Disaster Management (NDM) scenarios, where rapid and accurate image retrieval can assist in monitoring disaster events and supporting effective emergency response. We present a novel Concept-Based CBIR (CCBIR) pipeline designed to enhance the CBIR process by integrating semantic image concepts derived from image features. The pipeline extracts features using a task-specific DNN backbone and applies Non-Negative Matrix Factorization (NMF) to derive semantic image concepts. For efficient image retrieval based on concept based image similarity, the FAISS library is leveraged, which enables fast approximate nearest neighbor search in high-dimensional spaces. By transforming complex, high-dimensional features into a structured and interpretable concept space, the proposed CCBIR approach facilitates a more precise alignment between computational image representations and human semantic understanding. The pipeline has been evaluated on three datasets featuring different CBIR problems in natural disaster scenarios such as forest fires and floods, comparing favorably against state-of-the-art CBIR methods. This kind of performance showcases the effectiveness and adaptability of the CCBIR system, highlighting its potential to improve disaster response and recovery operations. • A Concept-Based CBIR (CCBIR) pipeline for content-based image retrieval is proposed. • Interpretable image concepts are extracted using Non-Negative Matrix Factorization. • Our method improves retrieval accuracy and interpretability compared to state-of-the-art. • The pipeline achieves superior performance in flood and wildfire retrieval tasks.

In the realm of neural network research, achieving experiment reproducibility is paramount for building upon existing knowledge and advancing the field. This paper examines a multi-agent neural network framework on its ability to facilitate the reproduction of experiments. Also, we address the reproducibility problem when there are data or source code limitations. The framework offers crucial functionalities for facilitating experiment reproducibility achieved through data, layer outputs, architectures, and weights exchange among the framework's agents. Through the integration of these functionalities, this framework empowers researchers to reproduce and validate experimental results consistently, fostering a more robust and collaborative research environment in the field of neural networks. The experimental results demonstrate the framework's reproducibility abilities. Furthermore, we test the framework in terms of reproducibility in an emergency natural disaster management situation. Finally, we analyze how the privacy limitations of the original neural network affect the reproducibility results.

Recently, climate change has led to more frequent extreme weather events, introducing new challenges for Natural Disaster Management (NDM) organizations. This fact makes the employment of modern technological tools such as Deep Neural Networks-based fire detectors a necessity, as they can assist such organizations manage these extreme events more effectively. In this work, we argue that the mean Average Precision (mAP) metric that is commonly used to evaluate typical object detection algorithms can not be trusted for the fire detection task, due to its high dependence on the employed data annotation strategy. This means that the mAP score of a fire detection algorithm may be low even when it predicts fire bounding boxes that accurately enclose the depicted fires. In this direction, a new evaluation metric for fire detection is proposed, denoted as Image-level mean Average Precision (ImAP), which reduces the dependence on the bounding box annotation strategy by rewarding/penalizing bounding box predictions on image level, rather than on bounding box level. Experiments using different object detection algorithms have shown that the proposed ImAP metric reveals the true fire detection capabilities of the tested algorithms more effectively.

State-of-the-art deep neural unsupervised video summarization methods mostly fall under the adversarial reconstruction framework. This employs a Generative Adversarial Network (GAN) structure and Long Short-Term Memory (LSTM) autoencoders during its training stage. The typical result is a selector LSTM that sequentially receives video frame representations and outputs corresponding scalar importance factors, which are then used to select key-frames. This basic approach has been augmented with an additional Deep Reinforcement Learning (DRL) agent, trained using the Discriminator’s output as a reward, which learns to optimize the selector’s outputs. However, local minima are a well-known problem in DRL. Thus, this paper presents a novel regularizer for escaping local loss minima, in order to improve unsupervised key-frame extraction. It is an additive loss term employed during a second training phase, that rewards the difference of the neural agent’s parameters from those of a previously found good solution. Thus, it encourages the training process to explore more aggressively the parameter space in order to discover a better local loss minimum. Evaluation performed on two public datasets shows considerable increases over the baseline and against the state-of-the-art.

In this work we propose the use of local binary patterns in combination with double spectral analysis for facial image clustering applied to 3D (stereoscopic) videos. Double spectral clustering involves the fusion of two well known algorithms: Normalized cuts and spectral clustering in order to improve the clustering performance. The use of local binary patterns upon selected fiducial points on the facial images proved to be a good choice for describing images. The framework is applied on 3D videos and makes use of the additional information deriving from the existence of two channels, left and right for further improving the clustering results.

In this paper, we present a novel method aiming at multidimensional sequence classification. We propose a novel sequence representation, based on its fuzzy distances from optimal representative signal instances, called statemes. We also propose a novel modified clustering discriminant analysis algorithm minimizing the adopted criterion with respect to both the data projection matrix and the class representation, leading to the optimal discriminant sequence class representation in a low-dimensional space, respectively. Based on this representation, simple classification algorithms, such as the nearest subclass centroid, provide high classification accuracy. A three step iterative optimization procedure for choosing statemes, optimal discriminant subspace and optimal sequence class representation in the final decision space is proposed. The classification procedure is fast and accurate. The proposed method has been tested on a wide variety of multidimensional sequence classification problems, including handwritten character recognition, time series classification and human activity recognition, providing very satisfactory classification results.