Análise de formas planas em imagens digitais: uma nova abordagem baseada na Transformada de Hough

Pós-graduação em Ciência da Computação - IBILCE

An Extended Kernel for Generalized Multiple-Instance Learning

The multiple-instance learning (MIL) model has been successful in areas such as drug discovery and content-based image-retrieval. Recently, this model was generalized and a corresponding kernel was introduced to learn generalized MIL concepts with a support vector machine. While this kernel enjoyed empirical success, it has limitations in its representation. We extend this kernel by enriching its representation and empirically evaluate our new kernel on data from content-based image retrieval, biological sequence analysis, and drug discovery. We found that our new kernel generalized noticeably better than the old one in content-based image retrieval and biological sequence analysis and was slightly better or even with the old kernel in the other applications, showing that an SVM using this kernel does not overfit despite its richer representation.

Class-specific metrics for multidimensional data projection applied to CBIR

Content-based image retrieval is still a challenging issue due to the inherent complexity of images and choice of the most discriminant descriptors. Recent developments in the field have introduced multidimensional projections to burst accuracy in the retrieval process, but many issues such as introduction of pattern recognition tasks and deeper user intervention to assist the process of choosing the most discriminant features still remain unaddressed. In this paper, we present a novel framework to CBIR that combines pattern recognition tasks, class-specific metrics, and multidimensional projection to devise an effective and interactive image retrieval system. User interaction plays an essential role in the computation of the final multidimensional projection from which image retrieval will be attained. Results have shown that the proposed approach outperforms existing methods, turning out to be a very attractive alternative for managing image data sets.

PRoSPer: perceptual similarity queries in medical CBIR systems through user profiles

In this paper, we present a novel approach to perform similarity queries over medical images, maintaining the semantics of a given query posted by the user. Content-based image retrieval systems relying on relevance feedback techniques usually request the users to label relevant/irrelevant images. Thus, we present a highly effective strategy to survey user profiles, taking advantage of such labeling to implicitly gather the user perceptual similarity. The profiles maintain the settings desired for each user, allowing tuning of the similarity assessment, which encompasses the dynamic change of the distance function employed through an interactive process. Experiments on medical images show that the method is effective and can improve the decision making process during analysis.

Geophotohunt: Studio di algoritmi di confronto di immagini per realizzare una caccia al tesoro fotografica

Questo studio si propone di realizzare un’applicazione per dispositivi Android che permetta, per mezzo di un gioco di ruolo strutturato come caccia al tesoro, di visitare in prima persona città d’arte e luoghi turistici. Gli utenti finali, grazie alle funzionalità dell’app stessa, potranno giocare, creare e condividere cacce al tesoro basate sulla ricerca di edifici, monumenti, luoghi di rilevanza artistico-storica o turistica; in particolare al fine di completare ciascuna tappa di una caccia al tesoro il giocatore dovrà scattare una fotografia al monumento o edificio descritto nell’obiettivo della caccia stessa. Il software grazie ai dati rilevati tramite GPS e giroscopio (qualora il dispositivo ne sia dotato) e per mezzo di un algoritmo di instance recognition sarà in grado di affermare se la foto scattata rappresenta la risposta corretta al quesito della tappa. L’applicazione GeoPhotoHunt rappresenta non solo uno strumento ludico per la visita di città turistiche o più in generale luoghi di interesse, lo studio propone, infatti come suo contributo originale, l’implementazione su piattaforma mobile di un Content Based Image Retrieval System (CBIR) del tutto indipendente da un supporto server. Nello specifico il server dell’applicazione non sarà altro che uno strumento di appoggio con il quale i membri della “community” di GeoPhotoHunt potranno pubblicare le cacce al tesoro da loro create e condividere i punteggi che hanno totalizzato partecipando a una caccia al tesoro. In questo modo quando un utente ha scaricato sul proprio smartphone i dati di una caccia al tesoro potrà iniziare l’avventura anche in assenza di una connessione internet. L’intero studio è stato suddiviso in più fasi, ognuna di queste corrisponde ad una specifica sezione dell’elaborato che segue. In primo luogo si sono effettuate delle ricerche, soprattutto nel web, con lo scopo di individuare altre applicazioni che implementano l’idea della caccia al tesoro su piattaforma mobile o applicazioni che implementassero algoritmi di instance recognition direttamente su smartphone. In secondo luogo si è ricercato in letteratura quali fossero gli algoritmi di riconoscimento di immagini più largamente diffusi e studiati in modo da avere una panoramica dei metodi da testare per poi fare la scelta dell’algoritmo più adatto al caso di studio. Quindi si è proceduto con lo sviluppo dell’applicazione GeoPhotoHunt stessa, sia per quanto riguarda l’app front-end per dispositivi Android sia la parte back-end server. Infine si è passati ad una fase di test di algoritmi di riconoscimento di immagini in modo di avere una sufficiente quantità di dati sperimentali da permettere di effettuare una scelta dell’algoritmo più adatto al caso di studio. Al termine della fase di testing si è deciso di implementare su Android un algoritmo basato sulla distanza tra istogrammi di colore costruiti sulla scala cromatica HSV, questo metodo pur non essendo robusto in presenza di variazioni di luminosità e contrasto, rappresenta un buon compromesso tra prestazioni, complessità computazionale in modo da rendere la user experience quanto più coinvolgente.

Комбинированный Подход к Представлению Содержания и Текстового Описания Мультимедиа

В статье рассмотрена проблема семантической разницы между содержимым мультимедиа и его текстовым описанием, определяемым вручную. Предложен комбинированный подход к представлению семантики мультимедиа, основанный на объединении близких по содержанию и текстовому описанию мультимедиа в классы, содержащие обобщённые описания объектов, связей между ними и ключевых слов текстовых метаданных из некоторого тезауруса. Для формирования этих классов используются операции иерархической кластеризации и машинного обучения. Данный подход позволяет расширить область поиска и навигации мультимедиа благодаря привлечению медиа-данных, имеющих схожее содержание и текстовое описание.

An Analysis of Colour Semantics in Art Images

This article presents the principal results of the Ph.D. thesis A Novel Method for Content-Based Image Retrieval in Art Image Collections Utilizing Colour Semantics by Krassimira Ivanova (Institute of Mathematics and Informatics, BAS), successfully defended at Hasselt Uni-versity in Belgium, Faculty of Science, on 15 November 2011.

Establishing Correspondences between Attribute Spaces and Complex Concept Spaces Using Meta-PGN Classifier

In this paper, we present one approach for extending the learning set of a classification algorithm with additional metadata. It is used as a base for giving appropriate names to found regularities. The analysis of correspondence between connections established in the attribute space and existing links between concepts can be used as a test for creation of an adequate model of the observed world. Meta-PGN classifier is suggested as a possible tool for establishing these connections. Applying this approach in the field of content-based image retrieval of art paintings provides a tool for extracting specific feature combinations, which represent different sides of artists' styles, periods and movements.

Classification of Paintings by Artist, Movement, and Indoor Setting Using MPEG-7 Descriptor Features

ACM Computing Classification System (1998): I.4.9, I.4.10.

O uso da Divergência de Kullback-Leibler e da Divergência Generalizada como medida de similaridade em sistemas CBIR

The content-based image retrieval is important for various purposes like disease diagnoses from computerized tomography, for example. The relevance, social and economic of image retrieval systems has created the necessity of its improvement. Within this context, the content-based image retrieval systems are composed of two stages, the feature extraction and similarity measurement. The stage of similarity is still a challenge due to the wide variety of similarity measurement functions, which can be combined with the different techniques present in the recovery process and return results that aren’t always the most satisfactory. The most common functions used to measure the similarity are the Euclidean and Cosine, but some researchers have noted some limitations in these functions conventional proximity, in the step of search by similarity. For that reason, the Bregman divergences (Kullback Leibler and I-Generalized) have attracted the attention of researchers, due to its flexibility in the similarity analysis. Thus, the aim of this research was to conduct a comparative study over the use of Bregman divergences in relation the Euclidean and Cosine functions, in the step similarity of content-based image retrieval, checking the advantages and disadvantages of each function. For this, it was created a content-based image retrieval system in two stages: offline and online, using approaches BSM, FISM, BoVW and BoVW-SPM. With this system was created three groups of experiments using databases: Caltech101, Oxford and UK-bench. The performance of content-based image retrieval system using the different functions of similarity was tested through of evaluation measures: Mean Average Precision, normalized Discounted Cumulative Gain, precision at k, precision x recall. Finally, this study shows that the use of Bregman divergences (Kullback Leibler and Generalized) obtains better results than the Euclidean and Cosine measures with significant gains for content-based image retrieval.

Aplicação para pesquisa de clip arts em dispositivos móveis baseada em esboços

Trabalho Final de Mestrado para obtenção do Grau de Mestre em Engenharia de Redes de Comunicação e Multimédia

A new re-ranking method using enhanced pseudo-relevance feedback for content-based medical image retrieval

We propose a novel re-ranking method for content-based medical image retrieval based on the idea of pseudo-relevance feedback (PRF). Since the highest ranked images in original retrieval results are not always relevant, a naive PRF based re-ranking approach is not capable of producing a satisfactory result. We employ a two-step approach to address this issue. In step 1, a Pearson's correlation coefficient based similarity update method is used to re-rank the high ranked images. In step 2, after estimating a relevance probability for each of the highest ranked images, a fuzzy SVM ensemble based approach is adopted to re-rank the images. The experiments demonstrate that the proposed method outperforms two other re-ranking methods.

A noisy-smoothing relevance feedback method for content-based medical image retrieval

In this paper, we address a new problem of noisy images which present in the procedure of relevance feedback for medical image retrieval. We concentrate on the noisy images, caused by the users mislabeling some irrelevant images as relevant ones, and a noisy-smoothing relevance feedback (NS-RF) method is proposed. In NS-RF, a two-step strategy is proposed to handle the noisy images. In step 1, a noisy elimination algorithm is adopted to identify and eliminate the noisy images. In step 2, to further alleviate the influence of noisy images, a fuzzy membership function is employed to estimate the relevance probabilities of retained relevant images. After noisy handling, the fuzzy support vector machine, which can take into account different relevant images with different relevance probabilities, is adopted to re-rank the images. The experimental results on the IRMA medical image collection demonstrate that the proposed method can deal with the noisy images effectively.

Construction site image retrieval based on material cluster recognition

The capability to automatically identify shapes, objects and materials from the image content through direct and indirect methodologies has enabled the development of several civil engineering related applications that assist in the design, construction and maintenance of construction projects. This capability is a product of the technological breakthroughs in the area of image processing that has allowed for the development of a large number of digital imaging applications in all industries. In this paper, an automated and content based construction site image retrieval method is presented. This method is based on image retrieval techniques, and specifically those related with material and object identification and matches known material samples with material clusters within the image content. The results demonstrate the suitability of this method for construction site image retrieval purposes and reveal the capability of existing image processing technologies to accurately identify a wealth of materials from construction site images.

Material-Based Construction Site Image Retrieval

The technological advancements in digital imaging, the widespread popularity of digital cameras, and the increasing demand by owners and contractors for detailed and complete site photograph logs have triggered an ever-increasing growth in the rate of construction image data collection, with thousands of images being stored for each project. However, the sheer volume of images and the difficulties in accurately and manually indexing them have generated a pressing need for methods that can index and retrieve images with minimal or no user intervention. This paper reports recent developments from research efforts in the indexing and retrieval of construction site images in architecture, engineering, construction, and facilities management image database systems. The limitations and benefits of the existing methodologies will be presented, as well as an explanation of the reasons for the development of a novel image retrieval approach that not only can recognize construction materials within the image content in order to index images, but also can be compatible with existing retrieval methods, enabling enhanced results.