A Benchmark for Image Retrieval using Distributed Systems over the Internet: BIRDS-I

The performance of CBIR algorithms is usually measured on an isolated workstation. In a real-world environment the algorithms would only constitute a minor component among the many interacting components. The Internet dramati-cally changes many of the usual assumptions about measuring CBIR performance. Any CBIR benchmark should be designed from a networked systems standpoint. These benchmarks typically introduce communication overhead because the real systems they model are distributed applications. We present our implementation of a client/server benchmark called BIRDS-I to measure image retrieval performance over the Internet. It has been designed with the trend toward the use of small personalized wireless systems in mind. Web-based CBIR implies the use of heteroge-neous image sets, imposing certain constraints on how the images are organized and the type of performance metrics applicable. BIRDS-I only requires controlled human intervention for the compilation of the image collection and none for the generation of ground truth in the measurement of retrieval accuracy. Benchmark image collections need to be evolved incrementally toward the storage of millions of images and that scaleup can only be achieved through the use of computer-aided compilation. Finally, our scoring metric introduces a tightly optimized image-ranking window.Comment: 24 pages, To appear in the Proc. SPIE Internet Imaging Conference 200

IDeixis : image-based deixis for recognizing locations

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 31-32).In this thesis, we describe an approach to recognizing location from camera-equipped mobile devices using image-based web search. This is an image-based deixis capable of pointing at a distant location away from the user's current location. We demonstrate our approach on an application allowing users to browse web pages matching the image of a nearby location. Common image search metrics can match images captured with a camera-equipped mobile device to images found on the World Wide Web. The users can recognize the location if those pages contain information about this location (e.g. name, facts, stories ... etc). Since the amount of information displayable on the device is limited, automatic keyword extraction methods can be applied to help efficiently identify relevant pieces of location information. Searching the entire web can be computationally overwhelming, so we devise a hybrid image-and-keyword searching technique. First, image-search is performed over images and links to their source web pages in a database that indexes only a small fraction of the web. Then, relevant keywords on these web pages are automatically identified and submitted to an existing text-based search engine (e.g. Google) that indexes a much larger portion of the web. Finally, the resulting image set is filtered to retain images close to the original query in terms of visual similarity. It is thus possible to efficiently search hundreds of millions of images that are not only textually related but also visually relevant.by Pei-Hsiu Yeh.S.M

MOBICORS-Movie: A MOBIle COntents Recommender System for Movie

In spite of the rapid growth of mobile multimedia contents market, most of the customers experience inconvenience, lengthy search processes and frustration in searching for the specific multimedia contents they want. These difficulties are attributable to the current mobile Internet service method based on inefficient sequential search. To overcome these difficulties, this paper proposes a MOBIle COntents Recommender System for Movie (MOBICORS-Movie), which is designed to reduce customers’ search efforts in finding desired movies on the mobile Internet. MOBICORS-Movie consists of three agents: CF (Collaborative Filtering), CBIR (Content-Based Information Retrieval) and RF (Relevance Feedback). These agents collaborate each other to support a customer in finding a desired movie by generating personalized recommendations of movies. To verify the performance of MOBICORS-Movie, the simulation-based experiments were conducted. The experiment results show that MOBICORS-Movie significantly reduces the customer’s search effort and can be a realistic solution for movie recommendation in the mobile Internet environment

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

A Brief Review On Image Retrieval Techniques and its Scope

This paper presents the novel approach for image retrieval. Image retrieval is an important problem in many applications, such as copyright infringement detection, tag annotation, commercial retrieval, and landmark identification. Image retrieval definition is given and the concept and significance of image retrieval is also provided. Various image retrieval techniques based on content based, sketch based, also based on image annotation is explained here. The last section includes the approach for retrieval is given as a problem formulation

Gesture based interface for image annotation

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Informática pela Universidade Nova de Lisboa, Faculdade de Ciências e TecnologiaGiven the complexity of visual information, multimedia content search presents more problems than textual search. This level of complexity is related with the difficulty of doing automatic image and video tagging, using a set of keywords to describe the content. Generally, this annotation is performed manually (e.g., Google Image) and the search is based on pre-defined keywords. However, this task takes time and can be dull. In this dissertation project the objective is to define and implement a game to annotate personal digital photos with a semi-automatic system. The game engine tags images automatically and the player role is to contribute with correct annotations. The application is composed by the following main modules: a module for automatic image annotation, a module that manages the game graphical interface (showing images and tags), a module for the game engine and a module for human interaction. The interaction is made with a pre-defined set of gestures, using a web camera. These gestures will be detected using computer vision techniques interpreted as the user actions. The dissertation also presents a detailed analysis of this application, computational modules and design, as well as a series of usability tests

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

Mobile Interface for Content-Based Image Management

People make more and more use of digital image acquisition devices to capture screenshots of their everyday life. The growing number of personal pictures raise the problem of their classification. Some of the authors proposed an automatic technique for personal photo album management dealing with multiple aspects (i.e., people, time and background) in a homogenous way. In this paper we discuss a solution that allows mobile users to remotely access such technique by means of their mobile phones, almost from everywhere, in a pervasive fashion. This allows users to classify pictures they store on their devices. The whole solution is presented, with particular regard to the user interface implemented on the mobile phone, along with some experimental results

Understanding User Intentions in Vertical Image Search

With the development of Internet and Web 2.0, large volume of multimedia contents have been made online. It is highly desired to provide easy accessibility to such contents, i.e. efficient and precise retrieval of images that satisfies users' needs. Towards this goal, content-based image retrieval (CBIR) has been intensively studied in the research community, while text-based search is better adopted in the industry. Both approaches have inherent disadvantages and limitations. Therefore, unlike the great success of text search, Web image search engines are still premature. In this thesis, we present iLike, a vertical image search engine which integrates both textual and visual features to improve retrieval performance. We bridge the semantic gap by capturing the meaning of each text term in the visual feature space, and re-weight visual features according to their significance to the query terms. We also bridge the user intention gap since we are able to infer the "visual meanings" behind the textual queries. Last but not least, we provide a visual thesaurus, which is generated from the statistical similarity between the visual space representation of textual terms. Experimental results show that our approach improves both precision and recall, compared with content-based or text-based image retrieval techniques. More importantly, search results from iLike are more consistent with users' perception of the query terms