A fast image retrieval method designed for network big data

In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method has a great improvement in the effective performance of feature extraction and can also get better search matching results

Internet cross-media retrieval based on deep learning

With the development of Internet, multimedia information such as image and video is widely used. Therefore, how to find the required multimedia data quickly and accurately in a large number of resources , has become a research focus in the field of information process. In this paper, we propose a real time internet cross-media retrieval method based on deep learning. As an innovation, we have made full improvement in feature extracting and distance detection. After getting a large amount of image feature vectors, we sort the elements in the vector according to their contribution and then eliminate unnecessary features. Experiments show that our method can achieve high precision in image-text cross media retrieval, using less retrieval time. This method has a great application space in the field of cross media retrieval

A knowledge acquisition tool to assist case authoring from texts.

Case-Based Reasoning (CBR) is a technique in Artificial Intelligence where a new problem is solved by making use of the solution to a similar past problem situation. People naturally solve problems in this way, without even thinking about it. For example, an occupational therapist (OT) that assesses the needs of a new disabled person may be reminded of a previous person in terms of their disabilities. He may or may not decide to recommend the same devices based on the outcome of an earlier (disabled) person. Case-based reasoning makes use of a collection of past problem-solving experiences thus enabling users to exploit the information of others successes and failures to solve their own problem(s). This project has developed a CBR tool to assist in matching SmartHouse technology to the needs of the elderly and people with disabilities. The tool makes suggestions of SmartHouse devices that could assist with given impairments. SmartHouse past problem-solving textual reports have been used to obtain knowledge for the CBR system. Creating a case-based reasoning system from textual sources is challenging because it requires that the text be interpreted in a meaningful way in order to create cases that are effective in problem-solving and to be able to reasonably interpret queries. Effective case retrieval and query interpretation is only possible if a domain-specific conceptual model is available and if the different meanings that a word can take can be recognised in the text. Approaches based on methods in information retrieval require large amounts of data and typically result in knowledge-poor representations. The costs become prohibitive if an expert is engaged to manually craft cases or hand tag documents for learning. Furthermore, hierarchically structured case representations are preferred to flat-structured ones for problem-solving because they allow for comparison at different levels of specificity thus resulting in more effective retrieval than flat structured cases. This project has developed SmartCAT-T, a tool that creates knowledge-rich hierarchically structured cases from semi-structured textual reports. SmartCAT-T highlights important phrases in the textual SmartHouse problem-solving reports and uses the phrases to create a conceptual model of the domain. The model then becomes a standard structure onto which each semi-structured SmartHouse report is mapped in order to obtain the correspondingly structured case. SmartCAT-T also relies on an unsupervised methodology that recognises word synonyms in text. The methodology is used to create a uniform vocabulary for the textual reports and the resulting harmonised text is used to create the standard conceptual model of the domain. The technique is also employed in query interpretation during problem solving. SmartCAT-T does not require large sets of tagged data for learning, and the concepts in the conceptual model are interpretable, allowing for expert refinement of knowledge. Evaluation results show that the created cases contain knowledge that is useful for problem solving. An improvement in results is also observed when the text and queries are harmonised. A further evaluation highlights a high potential for the techniques developed in this research to be useful in domains other than SmartHouse. All this has been implemented in the Smarter case-based reasoning system

Document management and retrieval for specialised domains : an evolutionary user-based approach

Browsing marked-up documents by traversing hyperlinks has become probably the most important means by which documents are accessed, both via the World Wide Web (WWW) and organisational Intranets. However, there is a pressing demand for document management and retrieval systems to deal appropriately with the massive number of documents available. There are two classes of solution: general search engines, whether for the WWW or an Intranet, which make little use of specific domain knowledge or hand-crafted specialised systems which are costly to build and maintain. The aim of this thesis was to develop a document management and retrieval system suitable for small communities as well as individuals in specialised domains on the Web. The aim was to allow users to easily create and maintain their own organisation of documents while ensuring continual improvement in the retrieval performance of the system as it evolves. The system developed is based on the free annotation of documents by users and is browsed using the concept lattice of Formal Concept Analysis (FCA). A number of annotation support tools were developed to aid the annotation process so that a suitable system evolved. Experiments were conducted in using the system to assist in finding staff and student home pages at the School of Computer Science and Engineering, University of New South Wales. Results indicated that the annotation tools provided a good level of assistance so that documents were easily organised and a lattice-based browsing structure that evolves in an ad hoc fashion provided good efficiency in retrieval performance. An interesting result suggested that although an established external taxonomy can be useful in proposing annotation terms, users appear to be very selective in their use of terms proposed. Results also supported the hypothesis that the concept lattice of FCA helped take users beyond a narrow search to find other useful documents. In general, lattice-based browsing was considered as a more helpful method than Boolean queries or hierarchical browsing for searching a specialised domain. We conclude that the concept lattice of Formal Concept Analysis, supported by annotation techniques is a useful way of supporting the flexible open management of documents required by individuals, small communities and in specialised domains. It seems likely that this approach can be readily integrated with other developments such as further improvements in search engines and the use of semantically marked-up documents, and provide a unique advantage in supporting autonomous management of documents by individuals and groups - in a way that is closely aligned with the autonomy of the WWW

FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013) March 24, 2013, Moscow, Russia

International audienceFormal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classifiation. The area came into being in the early 1980s and has since then spawned over 10000 scientific publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The Formal Concept Analysis Meets Information Retrieval (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval

Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications

The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations