Direct kernel biased discriminant analysis: a new content-based image retrieval relevance feedback algorithm

In recent years, a variety of relevance feedback (RF) schemes have been developed to improve the performance of content-based image retrieval (CBIR). Given user feedback information, the key to a RF scheme is how to select a subset of image features to construct a suitable dissimilarity measure. Among various RF schemes, biased discriminant analysis (BDA) based RF is one of the most promising. It is based on the observation that all positive samples are alike, while in general each negative sample is negative in its own way. However, to use BDA, the small sample size (SSS) problem is a big challenge, as users tend to give a small number of feedback samples. To explore solutions to this issue, this paper proposes a direct kernel BDA (DKBDA), which is less sensitive to SSS. An incremental DKBDA (IDKBDA) is also developed to speed up the analysis. Experimental results are reported on a real-world image collection to demonstrate that the proposed methods outperform the traditional kernel BDA (KBDA) and the support vector machine (SVM) based RF algorithms

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

Giving eyes to ICT!, or How does a computer recognize a cow?

Het door Schouten en andere onderzoekers op het CWI ontwikkelde systeem berust op het beschrijven van beelden met behulp van fractale meetkunde. De menselijke waarneming blijkt mede daardoor zo efficiënt omdat zij sterk werkt met gelijkenissen. Het ligt dus voor de hand het te zoeken in wiskundige methoden die dat ook doen. Schouten heeft daarom beeldcodering met behulp van 'fractals' onderzocht. Fractals zijn zelfgelijkende meetkundige figuren, opgebouwd door herhaalde transformatie (iteratie) van een eenvoudig basispatroon, dat zich daardoor op steeds kleinere schalen vertakt. Op elk niveau van detaillering lijkt een fractal op zichzelf (Droste-effect). Met fractals kan men vrij eenvoudig bedrieglijk echte natuurvoorstellingen maken. Fractale beeldcodering gaat ervan uit dat het omgekeerde ook geldt: een beeld effectief opslaan in de vorm van de basispatronen van een klein aantal fractals, samen met het voorschrift hoe het oorspronkelijke beeld daaruit te reconstrueren. Het op het CWI in samenwerking met onderzoekers uit Leuven ontwikkelde systeem is mede gebaseerd op deze methode. ISBN 906196502

Estimating Missing Features to Improve Multimedia Information Retrieval

Retrieval in a multimedia database usually involves combining information from different modalities of data, such as text and images. However, all modalities of the data may not be available to form the query. The retrieval results from such a partial query are often less than satisfactory. In this paper, we present an approach to complete a partial query by estimating the missing features in the query. Our experiments with a database of images and their associated captions show that, with an initial text-only query, our completion method has similar performance to a full query with both image and text features. In addition, when we use relevance feedback, our approach outperforms the results obtained using a full query

Модели, методи и алгоритми за създаване на система за управление на мултимедийно съдържание

ИМИ-БАН, 24.09.2012 г., присъждане на образователна и научна степен "доктор" на Янислав Панайотов Желев по научна специалност 01.01.12 информатика. [Zhelev Yanislav Panayotov; Желев Янислав Панайотов

Visual thesaurus for color image retrieval using SOM.

Yip King-Fung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 84-89).Abstracts in English and Chinese.Abstract --- p.i論文摘要 --- p.iiiTable of Contents --- p.ivList of Abbreviations --- p.viAcknowledgements --- p.viiChapter 1. --- Introduction --- p.1Chapter 1.1. --- Background --- p.1Chapter 1.2. --- Motivation --- p.3Chapter 1.3. --- Thesis Organization --- p.4Chapter 2. --- A Survey of Content-based Image Retrieval --- p.5Chapter 2.1. --- Text-based Image Retrieval --- p.5Chapter 2.2. --- Content-Based Image Retrieval --- p.7Chapter 2.2.1. --- Content-Based Image Retrieval Systems --- p.7Chapter 2.2.2. --- Query Methods --- p.9Chapter 2.2.3. --- Image Features --- p.11Chapter 2.2.4. --- Summary --- p.16Chapter 3. --- Visual Thesaurus using SOM --- p.17Chapter 3.1. --- Algorithm --- p.17Chapter 3.1.1. --- Image Representation --- p.17Chapter 3.1.2. --- Self-Organizing Map --- p.21Chapter 3.2. --- Preliminary Experiment --- p.27Chapter 3.2.1. --- Feature differences --- p.27Chapter 3.2.2. --- Labeling differences --- p.30Chapter 4. --- Experiment --- p.33Chapter 4.1. --- Subjects --- p.33Chapter 4.2. --- Apparatus --- p.33Chapter 4.2.1. --- Systems --- p.33Chapter 4.2.2. --- Test Databases --- p.33Chapter 4.3. --- Procedure --- p.34Chapter 4.3.1. --- Description --- p.35Chapter 4.3.2. --- SOM (text) --- p.36Chapter 4.3.3. --- SOM (image) --- p.38Chapter 4.3.4. --- QBE (text) --- p.40Chapter 4.3.5. --- QBE (image) --- p.42Chapter 4.3.6. --- Questionnaire --- p.44Chapter 4.3.7. --- Experiment Flow --- p.45Chapter 4.4. --- Results --- p.46Chapter 4.5. --- Discussion --- p.51Chapter 5. --- Quantizing Color Histogram --- p.55Chapter 5.1. --- Algorithm --- p.56Chapter 5.1.1. --- Codebook Generation Phrase --- p.57Chapter 5.1.2. --- Histogram Generation Phrase --- p.66Chapter 5.2. --- Experiment --- p.67Chapter 5.2.1. --- Test Database --- p.67Chapter 5.2.2. --- Evaluation Methods --- p.67Chapter 5.2.3. --- Results and Discussion --- p.69Chapter 5.2.4. --- Summary --- p.74Chapter 6. --- Relevance Feedback --- p.75Chapter 6.1. --- Relevance Feedback in Text Information Retrieval --- p.75Chapter 6.2. --- Relevance Feedback in Multimedia Information Retrieval --- p.76Chapter 6.3. --- Relevance Feedback in Visual Thesaurus --- p.76Chapter 7. --- Conclusions --- p.80Chapter 7.1. --- Applications --- p.81Chapter 7.2. --- Future Directions --- p.81Chapter 7.2.1. --- SOM Generation --- p.81Chapter 7.2.2. --- Hybrid Architecture --- p.82References --- p.8

Saliency for Image Description and Retrieval

We live in a world where we are surrounded by ever increasing numbers of images. More often than not, these images have very little metadata by which they can be indexed and searched. In order to avoid information overload, techniques need to be developed to enable these image collections to be searched by their content. Much of the previous work on image retrieval has used global features such as colour and texture to describe the content of the image. However, these global features are insufficient to accurately describe the image content when different parts of the image have different characteristics. This thesis initially discusses how this problem can be circumvented by using salient interest regions to select the areas of the image that are most interesting and generate local descriptors to describe the image characteristics in that region. The thesis discusses a number of different saliency detectors that are suitable for robust retrieval purposes and performs a comparison between a number of these region detectors. The thesis then discusses how salient regions can be used for image retrieval using a number of techniques, but most importantly, two techniques inspired from the field of textual information retrieval. Using these robust retrieval techniques, a new paradigm in image retrieval is discussed, whereby the retrieval takes place on a mobile device using a query image captured by a built-in camera. This paradigm is demonstrated in the context of an art gallery, in which the device can be used to find more information about particular images. The final chapter of the thesis discusses some approaches to bridging the semantic gap in image retrieval. The chapter explores ways in which un-annotated image collections can be searched by keyword. Two techniques are discussed; the first explicitly attempts to automatically annotate the un-annotated images so that the automatically applied annotations can be used for searching. The second approach does not try to explicitly annotate images, but rather, through the use of linear algebra, it attempts to create a semantic space in which images and keywords are positioned such that images are close to the keywords that represent them within the space