Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval

Analysis of Image Retrieval using Hybrid Texture and Shape based Feature Vectors

Content based image retrieval system is required so as to improve the retrieval of images from large web based and other various types of databases. With the increased importance of digital data, the production of image and video information has resulted in a large amount of images and videos that needs to be properly indexed for retrieval in the future. Various databases contain a large number of images, videos etc for which Content-Base retrieval systems or applications are greatly needed. To date, a number of methods and algorithms have been proposed by researchers from around the world to meet this challenge. This paper presents a literature review of such researches and a modified approach

Image Retrieval Using Content Information

Abstract: With the rapid development of the multimedia technologies such as compression and digital image sensor technologies, it is more and more necessary to build up a large database. Therefore robust and efficient methodology for image retrieval techniques has gained the attention of researchers. In the last few years the booming interest in the web images has paved a way to make people on content based Image retrieval techniques and also supporting systems for the retrieval process. In this paper, a general review of Content based image retrieval process by comparing with existing retrieval techniques along with experimental results and applications are presented. Some of the major challenges in the adaptation of content based image retrieval algorithms are also discussed

Applying digital content management to support localisation

The retrieval and presentation of digital content such as that on the World Wide Web (WWW) is a substantial area of research. While recent years have seen huge expansion in the size of web-based archives that can be searched efficiently by commercial search engines, the presentation of potentially relevant content is still limited to ranked document lists represented by simple text snippets or image keyframe surrogates. There is expanding interest in techniques to personalise the presentation of content to improve the richness and effectiveness of the user experience. One of the most significant challenges to achieving this is the increasingly multilingual nature of this data, and the need to provide suitably localised responses to users based on this content. The Digital Content Management (DCM) track of the Centre for Next Generation Localisation (CNGL) is seeking to develop technologies to support advanced personalised access and presentation of information by combining elements from the existing research areas of Adaptive Hypermedia and Information Retrieval. The combination of these technologies is intended to produce significant improvements in the way users access information. We review key features of these technologies and introduce early ideas for how these technologies can support localisation and localised content before concluding with some impressions of future directions in DCM

Content Based Image Retrieval by Using Interactive Relevance Feedback Technique - A Survey

Due to rapid increase in storing and capturing multimedia data with the digital device, Content Based Image Retrieval play a very important role in the field of image processing. Although wide ranging studies have been done in the field of CBIR but image finding from multimedia data basis is still very complicated and open problem. If paper provide an review of CBIR based on some of the famous techniques such as Interactive Genetic Algorithm, Relevance Feedback (RS), Neural Network and so on. Relevance Feedback can be used to enhance the ability of CBIR effectively by dropping the semantic gap between low level feature and high level feature. Interactiveness on CBIR can also be done with the help of Genetic Algorithms. GA is the branch of evolutionary computation which makes the retrieval process more interactive so that user can get advanced results from database by comparing to Query Image with its evaluation. The result of traditional implicit feedback can also be improved by Neuro Fuzzy Logic based implicit feedback. This paper covers all the aspect of Relevance Feedback (RF), Interactive Genetic Algorithms, Neural Network in Content Based Image Retrieval, various RF techniques and applications of CBIR. DOI: 10.17762/ijritcc2321-8169.15075

An Efficient Perceptual of Content Based Image Retrieval System Using SVM and Evolutionary Algorithms

The CBIR tends to index and retrieve images based on their visual content. CBIR avoids several issues related to traditional ways that of retrieving images by keywords. Thus, a growing interest within the area of CBIR has been established in recent years. The performance of a CBIR system mainly depends on the particular image representation and similarity matching operate utilized. The CBIR tends to index and retrieve images supported their visual content. CBIR avoids several issues related to traditional ways that of retrieving images by keywords. Thus, a growing interest within the area of CBIR has been established in recent years. The performance of a CBIR system principally depends on the actual image illustration and similarity matching operate utilized. therefore a replacement CBIR system is projected which can give accurate results as compared to previously developed systems. This introduces the new composite framework for image classification in a content-based image retrieval system. The projected composite framework uses an evolutionary algorithm to select training samples for support vector machine (SVM). to style such a system, the most popular techniques of content-based image retrieval are reviewed initial. Our review reveals some limitations of the existing techniques, preventing them to accurately address some issues

Deep Image Retrieval: A Survey

In recent years a vast amount of visual content has been generated and shared from various fields, such as social media platforms, medical images, and robotics. This abundance of content creation and sharing has introduced new challenges. In particular, searching databases for similar content, i.e.content based image retrieval (CBIR), is a long-established research area, and more efficient and accurate methods are needed for real time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of intelligent search. In this survey we organize and review recent CBIR works that are developed based on deep learning algorithms and techniques, including insights and techniques from recent papers. We identify and present the commonly-used benchmarks and evaluation methods used in the field. We collect common challenges and propose promising future directions. More specifically, we focus on image retrieval with deep learning and organize the state of the art methods according to the types of deep network structure, deep features, feature enhancement methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, aiming to promote a global view of the field of instance-based CBIR.Comment: 20 pages, 11 figure

Evolution of Information Retrieval System: Critical Review of Multimedia Information Retrieval System Based On Content, Context, and Concept

In recent years the explosive growth of information affects the flood of information. The amount of information must be followed by the development of the effective Information Retrieval System (IRS) so that the information will be easily accessible and useful for the user. The source of Information contains various media format, beside text there is also image, audio, and video that called multimedia. A large number of multimedia information rise the Multimedia Information Retrieval System (MIRS). Most of MIRS today is monolithic or only using one media format like Google1 for text search, tineye2 for image search, youtube3 for video search or 4shared4 for music and audio search. There is a need of information in any kind of media, not only retrieve the document in text format, but also retrieve the document in an image, audio and video format at once from any kind media format of the query. This study reviews the evolution of IRS, regress from text-based to concept- based MIRS. Unified Multimedia Indexing technique is discussed along with Concept-based MIRS. This critical review concludes that the evolution of IRS follows three paces: content-based, context-based and concept-based. Each pace takes on indexing system and retrieval techniques to optimize information retrieved. The challenge is how to come up with a retrieval technique that can process unified MIRS in order to retrieve optimally the relevant document