An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

The explosive growth in volume, velocity, and diversity of data produced by mobile devices and cloud applications has contributed to the abundance of data or `big data.' Available solutions for efficient data storage and management cannot fulfill the needs of such heterogeneous data where the amount of data is continuously increasing. For efficient retrieval and management, existing indexing solutions become inefficient with the rapidly growing index size and seek time and an optimized index scheme is required for big data. Regarding real-world applications, the indexing issue with big data in cloud computing is widespread in healthcare, enterprises, scientific experiments, and social networks. To date, diverse soft computing, machine learning, and other techniques in terms of artificial intelligence have been utilized to satisfy the indexing requirements, yet in the literature, there is no reported state-of-the-art survey investigating the performance and consequences of techniques for solving indexing in big data issues as they enter cloud computing. The objective of this paper is to investigate and examine the existing indexing techniques for big data. Taxonomy of indexing techniques is developed to provide insight to enable researchers understand and select a technique as a basis to design an indexing mechanism with reduced time and space consumption for BD-MCC. In this study, 48 indexing techniques have been studied and compared based on 60 articles related to the topic. The indexing techniques' performance is analyzed based on their characteristics and big data indexing requirements. The main contribution of this study is taxonomy of categorized indexing techniques based on their method. The categories are non-artificial intelligence, artificial intelligence, and collaborative artificial intelligence indexing methods. In addition, the significance of different procedures and performance is analyzed, besides limitations of each technique. In conclusion, several key future research topics with potential to accelerate the progress and deployment of artificial intelligence-based cooperative indexing in BD-MCC are elaborated on.

A survey on indexing techniques for big data: taxonomy and performance evaluation

Low-cost consumer radar integrated circuits combined with recent advances in machine learning have opened up a range of new possibilities in smart sensing. In this paper, we use a miniature radar sensor to capture Doppler signatures of 14 different hand gestures and train a deep convolutional neural network (DCNN) to classify these captured gestures. We utilize two receiving antennas of a continuous-wave Doppler radar capable of producing the in-phase and quadrature components of the beat signals. We map these two beat signals into three input channels of a DCNN as two spectrograms and an angle of arrival matrix. The classification results of the proposed architecture show a gesture classification accuracy exceeding 95% and a very low confusion between different gestures. This is almost 10% improvement over the single-channel Doppler methods reported in the literature.

Hand-Gesture Recognition Using Two-Antenna Doppler Radar With Deep Convolutional Neural Networks

Multimedia indexing systems based on semantic concept detectors are incomplete in the semantic sense. We can improve the effectiveness of these systems by using knowledge-based approaches which utilize semantic knowledge. In this paper, we propose a novel and efficient approach to enhance semantic concept detection in multimedia content, by exploiting contextual information about concepts from visual modality. First, a semantic knowledge is extracted via a contextual annotation framework. Second, a Fuzzy ontology is proposed to represent the fuzzy relationships (roles and rules) among every context and its semantic concepts. We use an abduction engine based on βeta function as a membership function for fuzzy rules. Third, a deduction engine is used to handle richer results in our video indexing system by running the proposed fuzzy ontology. Experiments on TRECVID 2010 benchmark have been performed to evaluate the performance of this approach. The obtained results show consistent improvement in semantic concepts detection, when a context space is used, and a good degree of indexing effectiveness as compared to existing approaches.

A fuzzy ontology: based framework for reasoning in visual video content analysis and indexing

A video retrieval system user hopes to find relevant information when the proposed queries are ambiguous. The retrieval process based on detecting concepts remains ineffective in such a situation. Potential relationships between concepts have been shown as a valuable knowledge resource that can enhance the retrieval effectiveness, even for ambiguous queries. Recent researches in multimedia retrieval have focused on ontology modeling as a common framework to manage knowledge. Handling these ontologies has to cope with issues related to generic knowledge management and processing scalability. Considering these issues, we suggest a context-based fuzzy ontology framework for video content analysis and indexing. In this paper, we focused on the way in which we modeled our fuzzy ontology: First, we populate automatically the generated ontology by gathering various available video annotation datasets. Then, the ontology content was used to infer enhanced video semantic interpretation. Finally, considering user feedback, the content of the ontology was improved. Experimental results showed that our approach achieves the goal of scalability while at the same time allowing better video content semantic interpretation.

Fuzzy reasoning framework to improve semantic video interpretation

In this paper, we describe an overview of a software platform that has been developed within REGIMVid project for TRECVID 2010 video retrieval experiments. The REGIMVID team participated in Semantic Indexing task. In TRECVID 2010, we explore several novel techniques to perform the detection of semantic concepts, including multi classifiers with supervised learning process, discriminative feature representation based on local keypoints, visual concept fusion using LSCOM rules, and also multimodal concept fusion using LSCOM Ontology.

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REGIMVID at TRECVID2010: Semantic Indexing.

Through the success of deep convolutional neural networks (CNN) for image classification and semantic concepts detection, the multimedia retrieval community provides interesting image analysing approaches and tools in order to deliver accurate semantic interpretation Never the less, such approaches still focusing only on explicit information and objects that exist in content Considering that implicit information could enhance the semantic interpretation, we are interested in a knowledge based framework to detect the semantic context In this paper, we discuss a fuzzy ontology based approach for understanding image content through the detection of the contained context We conducted preliminary experiments on the ImageNet2017 dataset While the obtained results still not impressive, many open research direction could be tackled Indeed, we think that a deep based knowledge management (in particular knowledge extraction and reasoning) could be considered as interesting and promising

A fuzzy ontology driven context classification system using large-scale image recognition based on deep CNN

In this paper, we describe our participation in the Image- CLEF 2015 Scalable Concept Image Annotation task. In this participa- tion, we display our approach for an automatic image annotation by the use of an ontology-based semantic hierarchy handled at both learning and annotation steps. While recent works focused on the use of semantic hierarchies to improve concept detector accuracy, we are investigating the use of such hierarchies to reduce detector complexity and then, to handle eciently large-scale image datasets. Our framework is based on two steps: (1) constructing a fuzzy ontology through analyzing learning dataset, and (2) guiding the annotation process through a reasoning en- gine. The obtained results conrm that this approach is promising for scalable image annotation.

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Mohamed Zarka

Papers

A fuzzy ontology: based framework for reasoning in visual video content analysis and indexing

Fuzzy reasoning framework to improve semantic video interpretation

REGIMVID at TRECVID2010: Semantic Indexing.

A fuzzy ontology driven context classification system using large-scale image recognition based on deep CNN

Regimvid at ImageCLEF 2015 Scalable Concept Image Annotation Task: Ontology based Hierarchical Image Annotation