Image indexing and retrieval using an ART-2A neural network architecture

Traditional content-based image retrieval (CBIR) systems use low-level features such as colors, shapes, and textures of images. Although, users make queries based on semantics, which are not easily related to such low-level characteristics. Recent works on CBIR confirm that researchers have been trying to map visual low-level characteristics and high-level semantics. The relation between low-level characteristics and image textual information has motivated this article which proposes a model for automatic classification and categorization of words associated to images. This proposal considers a self-organizing neural network architecture, which classifies textual information without previous learning. Experimental results compare the performance results of the text-based approach to an image retrieval system based on low-level features. (c) 2008 Wiley Periodicals, Inc.

Seleção seqüencial de descritores por análise da semântica para recuperação de imagens baseada no conteúdo

With the rapid growth of databases of various types (text, multimedia, etc..), There exist a need to propose methods for ordering, access and retrieve data in a simple and fast way. The images databases, in addition to these needs, require a representation of the images so that the semantic content characteristics are considered. Accordingly, several proposals such as the textual annotations based retrieval has been made. In the annotations approach, the recovery is based on the comparison between the textual description that a user can make of images and descriptions of the images stored in database. Among its drawbacks, it is noted that the textual description is very dependent on the observer, in addition to the computational effort required to describe all the images in database. Another approach is the content based image retrieval - CBIR, where each image is represented by low-level features such as: color, shape, texture, etc. In this sense, the results in the area of CBIR has been very promising. However, the representation of the images semantic by low-level features is an open problem. New algorithms for the extraction of features as well as new methods of indexing have been proposed in the literature. However, these algorithms become increasingly complex. So, doing an analysis, it is natural to ask whether there is a relationship between semantics and low-level features extracted in an image? and if there is a relationship, which descriptors better represent the semantic? which leads us to a new question: how to use descriptors to represent the content of the images?. The work presented in this thesis, proposes a method to analyze the relationship between low-level descriptors and semantics in an attempt to answer the questions before. Still, it was observed that there are three possibilities of indexing images: Using composed characteristic vectors, using parallel and independent index structures (for each descriptor or set of them) and using characteristic vectors sorted in sequential order. Thus, the first two forms have been widely studied and applied in literature, but there were no records of the third way has even been explored. So this thesis also proposes to index using a sequential structure of descriptors and also the order of these descriptors should be based on the relationship that exists between each descriptor and semantics of the users. Finally, the proposed index in this thesis revealed better than the traditional approachs and yet, was showed experimentally that the order in this sequence is important and there is a direct relationship between this order and the relationship of low-level descriptors with the semantics of the users

Modes of cell death in the hypopharyngeal gland of the honey bee (Apis mellifera L)

Different modes of cell death have been revealed in the regressing hypopharyngeal glands of worker honey bees. The hypopharyngeal gland, which is well developed in young nursing bees to produce protein for larval food, was seen to regress naturally in foraging adult worker bees. A range of techniques including histology, cytochemistry, in situ TUNEL, Annexin V and Comet assays indicated that cells within the gland demonstrate progressive symptoms of apoptosis, necrosis and a vacuolar form of programmed cell death. The latter mode of cell death did not display chromatin margination, but was accompanied by an enhanced level of autophagic and hydrolytic activity in which a cytosolic source of acid phosphatase became manifest in the extra-cisternal spaces. Normal and annexin-positive cells were found to occur in the younger nursing bees, whilst necrosis and an aberrant vacuolar type of apoptosis predominated in the older foraging bees. The relevance of these results to the classification of programmed cell death is discussed. (C) 2000 Academic Press.

Morphological characterization of cell death during the ovary differentiation in worker honey bee

Cell death that occurs during ovary differentiation in the honeybee worker's larval development accounts for ovariole reabsorption. From a morphological standpoint, three modes of death were detected. Germinative cells in the ovarioles die by an apoptotic-like process, whereas the somatic cells die by an autophagic process, type 11 cell death; and during pupation, stromatic and ovarian capsular cells die through cytoplasmic disintegration, releasing their components into the hemolymph. These modes of cell death are in part determined by the pattern of tissue organization within which the cell occurs. (C) 2002 Elsevier B.V. Ltd. All rights reserved.

Morphological alteration of peritoneal mast cells and macrophages in the mouse peritoneal cavity during the early phases of an allergic inflammatory reaction

We investigated the presence of mast cell granules in macrophages following an in vivo model of an allergic reaction. Injection of ovalbumin (100 mug) into the peritoneal cavity of sensitised mice produced a rapid (within 2 h) influx of neutrophils followed by a slower (after >4 h) eosinophil migration. Ovalbumin treatment induced a high incidence (similar to 50%) of mast cell degranulation compared to control phosphated-buffered saline-treated mice. The majority (similar to 90%) of peritoneal macrophages contained mast cell granules as early as 2 It post-ovalbumin, with lower values at later time-points, as determined by staining with Toluidine blue and Berberine sulphate. This was confirmed by electron microscopy which enabled us to identify the complex mast cell granule sub-structural components in macrophage phagosomes. In conclusion, we used histochemical and ultrastructural analyses to show that mast cell granules become internalised with macrophages during the early stages of an experimental allergic reaction. (C) 2001 Academic Press.

Representação, acesso, uso e reuso da imagem digital

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A scalable re-ranking method for content-based image retrieval

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Image Re-Ranking Acceleration on GPUs

Huge image collections are becoming available lately. In this scenario, the use of Content-Based Image Retrieval (CBIR) systems has emerged as a promising approach to support image searches. The objective of CBIR systems is to retrieve the most similar images in a collection, given a query image, by taking into account image visual properties such as texture, color, and shape. In these systems, the effectiveness of the retrieval process depends heavily on the accuracy of ranking approaches. Recently, re-ranking approaches have been proposed to improve the effectiveness of CBIR systems by taking into account the relationships among images. The re-ranking approaches consider the relationships among all images in a given dataset. These approaches typically demands a huge amount of computational power, which hampers its use in practical situations. On the other hand, these methods can be massively parallelized. In this paper, we propose to speedup the computation of the RL-Sim algorithm, a recently proposed image re-ranking approach, by using the computational power of Graphics Processing Units (GPU). GPUs are emerging as relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. We address the image re-ranking performance challenges by proposing a parallel solution designed to fit the computational model of GPUs. We conducted an experimental evaluation considering different implementations and devices. Experimental results demonstrate that significant performance gains can be obtained. Our approach achieves speedups of 7x from serial implementation considering the overall algorithm and up to 36x on its core steps.

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.

A scalable approach for content based image retrieval in cloud datacenter

The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops.

Extracción y recuperación de contenidos basado en vídeo en cirugía de mínima invasión

La introducción de las cirugías de mínima invasión en rutina clínica ha provocado la incorporación de los sistemas de vídeo dentro del quirófano. Así, estas técnicas proporcionan al cirujano imágenes que antes solo podían ser vistas mediante cirugía abierta. Los vídeos obtenidos en las intervenciones son almacenados en repositorios. El uso posterior de estos vídeos se ve limitado generalmente a su reproducción, debido a las dificultades de clasificación y gestión. La información que contienen estos repositorios puede ser explotada, reutilizando el conocimiento obtenido en cirugías similares. En este artículo de investigación se presenta el diseño de un módulo de gestión de conocimiento (MGC) para un repositorio de vídeos de cirugía de mínima invasión (CMI). El objetivo del módulo es gestionar y reutilizar la información contenida en el repositorio de vídeos laparoscópicos, para que puedan ser utilizadas con las experiencias previas en entornos de formación de nuevos cirujanos. Para este fin, se han implementado técnicas de recuperación de imagen y vídeo basadas en sus contenidos visuales (CBIR y CBVR). El MGC permite la recuperación de imágenes/vídeos, proporcionando información sobre la tarea que se está realizando en la escena quirúrgica. Los resultados obtenidos en este trabajo muestran la posibilidad de recuperar vídeos de CMI, a partir del instrumental presente en la escena quirúrgica.

An information foraging theory based user study of an adaptive user interaction framework for content-based image retrieval

This paper presents the design and results of a task-based user study, based on Information Foraging Theory, on a novel user interaction framework - uInteract - for content-based image retrieval (CBIR). The framework includes a four-factor user interaction model and an interactive interface. The user study involves three focused evaluations, 12 simulated real life search tasks with different complexity levels, 12 comparative systems and 50 subjects. Information Foraging Theory is applied to the user study design and the quantitative data analysis. The systematic findings have not only shown how effective and easy to use the uInteract framework is, but also illustrate the value of Information Foraging Theory for interpreting user interaction with CBIR. © 2011 Springer-Verlag Berlin Heidelberg.

Applying Information Foraging Theory to understand user interaction with content-based image retrieval

The paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data. Copyright 2010 ACM.

A four-factor user interaction model for content-based image retrieval

In order to bridge the “Semantic gap”, a number of relevance feedback (RF) mechanisms have been applied to content-based image retrieval (CBIR). However current RF techniques in most existing CBIR systems still lack satisfactory user interaction although some work has been done to improve the interaction as well as the search accuracy. In this paper, we propose a four-factor user interaction model and investigate its effects on CBIR by an empirical evaluation. Whilst the model was developed for our research purposes, we believe the model could be adapted to any content-based search system.

Enabling effective user interactions in content-based image retrieval

This paper presents an interactive content-based image retrieval framework—uInteract, for delivering a novel four-factor user interaction model visually. The four-factor user interaction model is an interactive relevance feedback mechanism that we proposed, aiming to improve the interaction between users and the CBIR system and in turn users overall search experience. In this paper, we present how the framework is developed to deliver the four-factor user interaction model, and how the visual interface is designed to support user interaction activities. From our preliminary user evaluation result on the ease of use and usefulness of the proposed framework, we have learnt what the users like about the framework and the aspects we could improve in future studies. Whilst the framework is developed for our research purposes, we believe the functionalities could be adapted to any content-based image search framework.