Intelligent Image Retrieval Techniques: A Survey

AbstractIn the current era of digital communication, the use of digital images has increased for expressing, sharing and interpreting information. While working with digital images, quite often it is necessary to search for a specific image for a particular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of images but it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same content-based searching task becomes extremely complex when the number of images is in the millions. To deal with the situation, some intelligent way of content-based searching is required to fulfill the searching request with right visual contents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficient and robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers who conducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques

Interactive search techniques for content-based retrieval from archives of images

Through a little investigation by file types it is possible to easily find that one of the most popular search engines has in its indexes about 10 billion of images. Even considering that this data is probably an underestimate of the real number, however, immediately it gives us an idea of how the images are a key component in human communication. This so exorbitant number puts us in the face of the enormous difficulties encountered when one has to deal with them. Until now, the images have always been accompanied by textual data: description, tags, labels, ... which are used to retrieve them fromthe archives. However it is clear that their increase, occurred in recent years, does not allow this type cataloguing. Furthermore, for its own nature, a manual cataloguing is subjective, partial and without doubt subject to error. To overcome this situation in recent years it has gotten a footing a kind of search based on the intrinsic characteristics of images such as colors and shapes. This information is then converted into numerical vectors, and through their comparison it is possible to find images that have similar characteristics. It is clear that a search, on this level of representation of the images, is far from the user perception that of the images. To allow the interaction between users and retrieval systems and improve the performance, it has been decided to involve the user in the search allowing to him to give a feedback of relevance of the images retrieved so far. In this the kind of image that are interesting for user can be learnt by the system and an improvement in the next iteration can be obtained. These techniques, although studied for many years, still present open issues. High dimensional feature spaces, lack of relevant training images, and feature spaceswith lowdiscriminative capability are just some of the problems encountered. In this thesis these problems will be faced by proposing some innovative solutions both to improve performance obtained by methods proposed in the literature, and to provide to retrieval systems greater generalization capability. Techniques of data fusion, both at the feature space level and at the level of different retrieval techniques, will be presented, showing that the former allow greater discriminative capability while the latter provide more robustness to the system. To overcome the lack of images of training it will be proposed a method to generate synthetic patterns allowing in this way a more balanced learning. Finally, new methods to measure similarity between images and to explore more efficiently the feature space will be proposed. The presented results show that the proposed approaches are indeed helpful in resolving some of the main problems in content based image retrieval

A picture is worth a thousand words : content-based image retrieval techniques

In my dissertation I investigate techniques for improving the state of the art in content-based image retrieval. To place my work into context, I highlight the current trends and challenges in my field by analyzing over 200 recent articles. Next, I propose a novel paradigm called __artificial imagination__, which gives the retrieval system the power to imagine and think along with the user in terms of what she is looking for. I then introduce a new user interface for visualizing and exploring image collections, empowering the user to navigate large collections based on her own needs and preferences, while simultaneously providing her with an accurate sense of what the database has to offer. In the later chapters I present work dealing with millions of images and focus in particular on high-performance techniques that minimize memory and computational use for both near-duplicate image detection and web search. Finally, I show early work on a scene completion-based image retrieval engine, which synthesizes realistic imagery that matches what the user has in mind.LEI Universiteit LeidenNWOImagin