Sistema para pesquisa de imagens com retroacção de relevância

Recentemente, a retroacção de relevância tem sido utilizada para melhorar o desempenho dos sistemas de pesquisa em base de dados de imagens. Este artigo apresenta um método de Retroacção de Relevância baseado no classificador de Mínimos Quadrados Regularizado e numa técnica de selecção de imagens que permite aumentar a capacidade de aprendizagem do método. São apresentados resultados de testes experimentais.info:eu-repo/semantics/publishedVersio

An adaptive technique for content-based image retrieval

We discuss an adaptive approach towards Content-Based Image Retrieval. It is based on the Ostensive Model of developing information needs—a special kind of relevance feedback model that learns from implicit user feedback and adds a temporal notion to relevance. The ostensive approach supports content-assisted browsing through visualising the interaction by adding user-selected images to a browsing path, which ends with a set of system recommendations. The suggestions are based on an adaptive query learning scheme, in which the query is learnt from previously selected images. Our approach is an adaptation of the original Ostensive Model based on textual features only, to include content-based features to characterise images. In the proposed scheme textual and colour features are combined using the Dempster-Shafer theory of evidence combination. Results from a user-centred, work-task oriented evaluation show that the ostensive interface is preferred over a traditional interface with manual query facilities. This is due to its ability to adapt to the user's need, its intuitiveness and the fluid way in which it operates. Studying and comparing the nature of the underlying information need, it emerges that our approach elicits changes in the user's need based on the interaction, and is successful in adapting the retrieval to match the changes. In addition, a preliminary study of the retrieval performance of the ostensive relevance feedback scheme shows that it can outperform a standard relevance feedback strategy in terms of image recall in category search

Multimedia resource discovery

This chapter examines the challenges and opportunities of Multimedia Information Retrieval and corresponding search engine applications. Computer technology has changed our access to information tremendously: We used to search authors or titles (which we had to know) in library cards in order to locate relevant books; now we can issue keyword searches within the full text of whole book repositories in order to identify authors, titles and locations of relevant books. What about the corresponding challenge of finding multimedia by fragments, examples and excerpts? Rather than asking for a music piece by artist and title, can we hum its tune to find it? Can doctors submit scans of a patient to identify medically similar images of diagnosed cases in a database? Can your mobile phone take a picture of a statue and tell you about its artist and significance via a service that it sends this picture to? In an attempt to answer some of these questions we get to know basic concepts of multimedia resource discovery technologies for a number of different query and document types: piggy-back text search, i.e., reducing the multimedia to pseudo text documents; automated annotation of visual components; content-based retrieval where the query is an image; and fingerprinting to match near duplicates. Some of the research challenges are given by the semantic gap between the simple pixel properties computers can readily index and high-level human concepts; related to this is an inherent technological limitation of automated annotation of images from pixels alone. Other challenges are given by polysemy, i.e., the many meanings and interpretations that are inherent in visual material and the corresponding wide range of a user’s information need. This chapter demonstrates how these challenges can be tackled by automated processing and machine learning and by utilising the skills of the user, for example through browsing or through a process that is called relevance feedback, thus putting the user at centre stage. The latter is made easier by “added value” technologies, exemplified here by summaries of complex multimedia objects such as TV news, information visualisation techniques for document clusters, visual search by example, and methods to create browsable structures within the collection

Iterative Refinement by Relevance Feedback in Content-Based Digital Image retrieval

Many image-database retrieval systems rely heavily on the success of one-shot queries, using optimised feature sets to obtain the best possible results. What is often missing from this approach is acceptance of the fact that the user knows considerably more about the query being made than can be conveyed in such relatively simple terms. If the query fails then the user must try and improve the description using only the available feature descriptors. This pape

Flexible photo retrieval (FlexPhoReS) : a prototype for multimodel personal digital photo retrieval

Digital photo technology is developing rapidly and is motivating more people to have large personal collections of digital photos. However, effective and fast retrieval of digital photos is not always easy, especially when the collections grow into thousands. World Wide Web (WWW) is one of the platforms that allows digital photo users to publish a collection of photos in a centralised and organised way. Users typically find their photos by searching or browsing uSing a keyboard and mouse. Also in development at the moment are alternative user interfaces such as graphical user interfaces with speech (S/GUI) and other multimodal user interfaces which offer more flexibility to users. The aim of this research was to design and evaluate a flexible user interface for a web based personal digital photo retrieval system. A model of a flexible photo retrieval system (FlexPhoReS) was developed based on a review of the literature and a small-scale user study. A prototype, based on the model, was built using MATLAB and WWW technology. FlexPhoReS is a web based personal digital photo retrieval prototype that enables digital photo users to . accomplish photo retrieval tasks (browsing, keyword and visual example searching (CBI)) using either mouse and keyboard input modalities or mouse and speech input modalities. An evaluation with 20 digital photo users was conducted using usability testing methods. The result showed that there was a significant difference in search performance between using mouse and keyboard input modalities and using mouse and speech input modalities. On average, the reduction in search performance time due to using mouse and speech input modalities was 37.31%. Participants were also significantly more satisfied with mouse and speech input modalities than with mouse and keyboard input modalities although they felt that both were complementary. This research demonstrated that the prototype was successful in providing a flexible model of the photo retrieval process by offering alternative input modalities through a multimodal user interface in the World Wide Web environment.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An object-based approach to retrieval of image and video content

Promising new directions have been opened up for content-based visual retrieval in recent years. Object-based retrieval which allows users to manipulate video objects as part of their searching and browsing interaction, is one of these. It is the purpose of this thesis to constitute itself as a part of a larger stream of research that investigates visual objects as a possible approach to advancing the use of semantics in content-based visual retrieval. The notion of using objects in video retrieval has been seen as desirable for some years, but only very recently has technology started to allow even very basic object-location functions on video. The main hurdles to greater use of objects in video retrieval are the overhead of object segmentation on large amounts of video and the issue of whether objects can actually be used efficiently for multimedia retrieval. Despite this, there are already some examples of work which supports retrieval based on video objects. This thesis investigates an object-based approach to content-based visual retrieval. The main research contributions of this work are a study of shot boundary detection on compressed domain video where a fast detection approach is proposed and evaluated, and a study on the use of objects in interactive image retrieval. An object-based retrieval framework is developed in order to investigate object-based retrieval on a corpus of natural image and video. This framework contains the entire processing chain required to analyse, index and interactively retrieve images and video via object-to-object matching. The experimental results indicate that object-based searching consistently outperforms image-based search using low-level features. This result goes some way towards validating the approach of allowing users to select objects as a basis for searching video archives when the information need dictates it as appropriate