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MFS-Map: efficient context and content combination to annotate images

Costa, Alceu Ferraz; Traina, Agma Juci Machado; Traina Junior, Caetano
Fonte: Association for Computing Machinery - ACM; Dongguk University; Gyeongju Publicador: Association for Computing Machinery - ACM; Dongguk University; Gyeongju
Tipo: Conferência ou Objeto de Conferência
Português
Relevância na Pesquisa
48.132925%
Automatic image annotation provides textual description to images based on content and context information. Since images may present large variability, image annotation methods often employ multiple extractors to represent visual contente considering local and global features under different visual aspects. As result, an important aspect of image annotation is the combination of context and content representations. This paper proposes MFS-Map (Multi-Feature Space Map), a novel image annotation method that manages the problem of combining multiple content and contexto representations when annotating images. The advantage of MFS-Map is that it does not represent visual and textual features by a single large feature vector. Rather, MFS-Map divides the problem into feature subspaces. This approach allows MFS-Map to improve its accuracy by identifying the features relevant for each annotation. We evaluated MFSMap using two publicly available datasets: MIR Flickr and Image CLEF 2011. MFS-Map obtained both superior precision and faster speed when compared to other widely employed annotation methods.; FAPESP (São Paulo State Research Foundation); CNPq (Brazilian National Research Council); CAPES (Brazilian Coordination for Improvement of Higher Level Personnel)

Mapeamento e documentação de feições visuais diagnósticas para interpretação em sistema baseado em conhecimento no domínio da petrografia; The diagnostic visual feature mapping and documentation in a knowledge-base system for interpretation in the Petrographic domain

Victoreti, Felipe Ingletto
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Dissertação Formato: application/pdf
Português
Relevância na Pesquisa
47.99985%
Nos domínios visuais, interpretações são iniciadas pela identificação de feições em imagens que irão, posteriormente, dar suporte aos processos mais abstratos de inferência. Para desenvolver sistemas de conhecimento neste tipo de domínio é necessário buscar a melhor representação do conhecimento visual para ser utilizado pelos métodos de inferência. A representação em formato simbólico deste conhecimento auxilia na captura do conhecimento implícito presente em imagens, permitindo seu uso nos processos de raciocínio, mesmo aceitando que parte desse conhecimento não é externalizado e, em conseqüência, não poderá ser representado adequadamente. Estudos recentes têm utilizado anotação de imagens como uma maneira capaz de auxiliar na explicitação do conhecimento, ampliando a expressividade dos formalismos de representação e permitindo o registro das informações associadas às imagens. Embora anotações de imagens flexibilizem a captura do conhecimento, ontologias são associadas às anotações para garantir a formalização do conhecimento nas imagens, suprindo os termos de domínio que podem ser usados para anotar e auxiliar a uniformização da linguagem nas consultas. O objetivo desse trabalho é capturar e documentar o conhecimento visual que dá suporte à inferência nas tarefas de interpretações. Nesse trabalho é elaborada uma maneira de identificar objetos em imagens que contenham feições diagnósticas através da utilização de uma ontologia de domínio pré-existente. Essa identificação de objetos é explorada para permitir a localização física de uma determinada feição em um objeto real. O resultado disso é a identificação de feições em uma imagem tendo-se um referencial de posição segundo um sistema de coordenadas espacial...

Anotação automática de imagens utilizando regras de associação; Automatic image annotation using associative rules

Guilherme Moraes Armigliatto
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 17/06/2011 Português
Relevância na Pesquisa
57.703535%
Com os avanços tecnológicos, grandes coleções de imagens são geradas, manipuladas e armazenadas em bancos de dados. Dado o grande tamanho destes bancos, verifica-se a necessidade de se criar ferramentas para gerenciá-los de forma eficiente e eficaz. Uma das tarefas mais demandadas deste gerenciamento é a recuperação das imagens, e uma forma de fazê-la é baseada no uso de anotações textuais associadas às imagens (por exemplo, palavras-chave e categorias). Entretanto, a anotação manual de grandes coleções de imagens apresenta vários problemas, como o alto consumo de tempo e a não padronização dos termos utilizados. Desse modo, esta dissertação apresenta quatro novos métodos para anotação automática de imagens, que visam amenizar estes problemas. Estes métodos utilizam as abordagens de descritores de imagens, dicionários visuais, programação genética e regras de associação. Os descritores e os dicionários são utilizados para representar as propriedades visuais das imagens, a programação genética é usada para combinar estas características e as regras de associação são usadas para relacioná-las com anotações. A principal contribuição desta dissertação consiste na análise do comportamento das regras de associação utilizadas para anotação de imagens em um conjunto de experimentos. Resultados experimentais demonstraram que os métodos propostos apresentam desempenho comparável ou superior ao de técnicas tradicionais da literatura.; With technological advances...

Gesture based interface for image annotation

Gonçalves, Duarte Nuno de Jesus
Fonte: Faculdade de Ciências e Tecnologia Publicador: Faculdade de Ciências e Tecnologia
Tipo: Dissertação de Mestrado
Publicado em //2008 Português
Relevância na Pesquisa
67.918867%
Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Informática pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia; Given 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...

Managing and Querying Image Annotation and Markup in XML

Wang, Fusheng; Pan, Tony; Sharma, Ashish; Saltz, Joel
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 01/01/2010 Português
Relevância na Pesquisa
47.918867%
Proprietary approaches for representing annotations and image markup are serious barriers for researchers to share image data and knowledge. The Annotation and Image Markup (AIM) project is developing a standard based information model for image annotation and markup in health care and clinical trial environments. The complex hierarchical structures of AIM data model pose new challenges for managing such data in terms of performance and support of complex queries. In this paper, we present our work on managing AIM data through a native XML approach, and supporting complex image and annotation queries through native extension of XQuery language. Through integration with xService, AIM databases can now be conveniently shared through caGrid.

Automatic Multilevel Medical Image Annotation and Retrieval

Mueen, A.; Zainuddin, R.; Baba, M. Sapiyan
Fonte: Springer-Verlag Publicador: Springer-Verlag
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
48.181104%
Image retrieval at the semantic level mostly depends on image annotation or image classification. Image annotation performance largely depends on three issues: (1) automatic image feature extraction; (2) a semantic image concept modeling; (3) algorithm for semantic image annotation. To address first issue, multilevel features are extracted to construct the feature vector, which represents the contents of the image. To address second issue, domain-dependent concept hierarchy is constructed for interpretation of image semantic concepts. To address third issue, automatic multilevel code generation is proposed for image classification and multilevel image annotation. We make use of the existing image annotation to address second and third issues. Our experiments on a specific domain of X-ray images have given encouraging results.

Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis

Iakovidis, D. K.; Goudas, T.; Smailis, C.; Maglogiannis, I.
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Publicado em 27/01/2014 Português
Relevância na Pesquisa
47.99985%
Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.

Multilabel Image Annotation Based on Double-Layer PLSA Model

Zhang, Jing; Li, Da; Hu, Weiwei; Chen, Zhihua; Yuan, Yubo
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
47.878696%
Due to the semantic gap between visual features and semantic concepts, automatic image annotation has become a difficult issue in computer vision recently. We propose a new image multilabel annotation method based on double-layer probabilistic latent semantic analysis (PLSA) in this paper. The new double-layer PLSA model is constructed to bridge the low-level visual features and high-level semantic concepts of images for effective image understanding. The low-level features of images are represented as visual words by Bag-of-Words model; latent semantic topics are obtained by the first layer PLSA from two aspects of visual and texture, respectively. Furthermore, we adopt the second layer PLSA to fuse the visual and texture latent semantic topics and achieve a top-layer latent semantic topic. By the double-layer PLSA, the relationships between visual features and semantic concepts of images are established, and we can predict the labels of new images by their low-level features. Experimental results demonstrate that our automatic image annotation model based on double-layer PLSA can achieve promising performance for labeling and outperform previous methods on standard Corel dataset.

Supervised learning of semantic classes for image annotation and retrieval

Carneiro, G.; Chan, A.; Moreno, P.; Vasconcelos, N.
Fonte: IEEE Computer Soc Publicador: IEEE Computer Soc
Tipo: Artigo de Revista Científica
Publicado em //2007 Português
Relevância na Pesquisa
67.99985%
A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally...

Context Dependent SVMs for Interconnected Image Network Annotation

Sahbi, H.; Li, X.
Fonte: ACM; New York Publicador: ACM; New York
Tipo: Conference paper
Publicado em //2010 Português
Relevância na Pesquisa
47.823076%
The exponential growth of interconnected networks, such as Flickr, currently makes them the standard way to share and explore data where users put contents and refer to others. These interconnections create valuable information in order to enhance the performance of many tasks in information retrieval including ranking and annotation. We introduce in this paper a novel image annotation framework based on support vector machines (SVMs) and a new class of kernels referred to as context-dependent. The method goes beyond the naive use of the intrinsic low level features (such as color, texture, shape, etc.) and context-free kernels, in order to design a kernel function applicable to interconnected databases such as social networks. The main contribution of our method includes a variational framework which helps designing this function using both intrinsic features and the underlying contextual information. This function also converges to a positive definite fixed-point, usable for SVM training and other kernel methods. When plugged in SVMs, our context-dependent kernel consistently improves the performance of image annotation, compared to context-free kernels, on hundreds of thousands of Flickr images.; http://www.acmmm10.org/; Hichem Sahbi...

Love Thy Neighbors: Image Annotation by Exploiting Image Metadata

Johnson, Justin; Ballan, Lamberto; Li, Fei-Fei
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
47.82969%
Some images that are difficult to recognize on their own may become more clear in the context of a neighborhood of related images with similar social-network metadata. We build on this intuition to improve multilabel image annotation. Our model uses image metadata nonparametrically to generate neighborhoods of related images using Jaccard similarities, then uses a deep neural network to blend visual information from the image and its neighbors. Prior work typically models image metadata parametrically, in contrast, our nonparametric treatment allows our model to perform well even when the vocabulary of metadata changes between training and testing. We perform comprehensive experiments on the NUS-WIDE dataset, where we show that our model outperforms state-of-the-art methods for multilabel image annotation even when our model is forced to generalize to new types of metadata.; Comment: Accepted to ICCV 2015

Manifold regularized kernel logistic regression for web image annotation

Liu, W.; Liu, H.; Tao, D.; Wang, Y.; Lu, K.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 20/12/2013 Português
Relevância na Pesquisa
47.99985%
With the rapid advance of Internet technology and smart devices, users often need to manage large amounts of multimedia information using smart devices, such as personal image and video accessing and browsing. These requirements heavily rely on the success of image (video) annotation, and thus large scale image annotation through innovative machine learning methods has attracted intensive attention in recent years. One representative work is support vector machine (SVM). Although it works well in binary classification, SVM has a non-smooth loss function and can not naturally cover multi-class case. In this paper, we propose manifold regularized kernel logistic regression (KLR) for web image annotation. Compared to SVM, KLR has the following advantages: (1) the KLR has a smooth loss function; (2) the KLR produces an explicit estimate of the probability instead of class label; and (3) the KLR can naturally be generalized to the multi-class case. We carefully conduct experiments on MIR FLICKR dataset and demonstrate the effectiveness of manifold regularized kernel logistic regression for image annotation.; Comment: submitted to Neurocomputing

Web image annotation by diffusion maps manifold learning algorithm

Pourali, Neda
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 08/12/2014 Português
Relevância na Pesquisa
47.918867%
Automatic image annotation is one of the most challenging problems in machine vision areas. The goal of this task is to predict number of keywords automatically for images captured in real data. Many methods are based on visual features in order to calculate similarities between image samples. But the computation cost of these approaches is very high. These methods require many training samples to be stored in memory. To lessen this burden, a number of techniques have been developed to reduce the number of features in a dataset. Manifold learning is a popular approach to nonlinear dimensionality reduction. In this paper, we investigate Diffusion maps manifold learning method for web image auto-annotation task. Diffusion maps manifold learning method is used to reduce the dimension of some visual features. Extensive experiments and analysis on NUS-WIDE-LITE web image dataset with different visual features show how this manifold learning dimensionality reduction method can be applied effectively to image annotation.; Comment: 11 pages, 8 figures

Analysing Word Importance for Image Annotation

Gulati, Payal; Sharma, A. K.
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 20/06/2013 Português
Relevância na Pesquisa
47.918867%
Image annotation provides several keywords automatically for a given image based on various tags to describe its contents which is useful in Image retrieval. Various researchers are working on text based and content based image annotations [7,9]. It is seen, in traditional Image annotation approaches, annotation words are treated equally without considering the importance of each word in real world. In context of this, in this work, images are annotated with keywords based on their frequency count and word correlation. Moreover this work proposes an approach to compute importance score of candidate keywords, having same frequency count.; Comment: 4 pages, 3 figures, Published in IJCSI (International Journal of Computer Science Issues) Journal, Volume 10, Issue 1, No 2, January 2013

Adaptive Tag Selection for Image Annotation

He, Xixi; Li, Xirong; Yang, Gang; Xu, Jieping; Jin, Qin
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 17/09/2014 Português
Relevância na Pesquisa
47.94924%
Not all tags are relevant to an image, and the number of relevant tags is image-dependent. Although many methods have been proposed for image auto-annotation, the question of how to determine the number of tags to be selected per image remains open. The main challenge is that for a large tag vocabulary, there is often a lack of ground truth data for acquiring optimal cutoff thresholds per tag. In contrast to previous works that pre-specify the number of tags to be selected, we propose in this paper adaptive tag selection. The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth. Such a division allows us to estimate how many tags shall be selected from the novel set according to the tags that have been selected from the seen set. The effectiveness of the proposed method is justified by our participation in the ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground truth available for 207 tags, the benchmark evaluation shows that compared to the popular top-$k$ strategy which obtains an F-score of 0.122, adaptive tag selection achieves a higher F-score of 0.223. Moreover...

IAT - Image Annotation Tool: Manual

Ciocca, Gianluigi; Napoletano, Paolo; Schettini, Raimondo
Fonte: Universidade Cornell Publicador: Universidade Cornell
Tipo: Artigo de Revista Científica
Publicado em 18/02/2015 Português
Relevância na Pesquisa
47.823076%
The annotation of image and video data of large datasets is a fundamental task in multimedia information retrieval and computer vision applications. In order to support the users during the image and video annotation process, several software tools have been developed to provide them with a graphical environment which helps drawing object contours, handling tracking information and specifying object metadata. Here we introduce a preliminary version of the image annotation tools developed at the Imaging and Vision Laboratory.

Integration of color, edge, shape, and texture features for automatic region-based image annotation and retrieval

Saber, Eli; Tekalp, A. Murat
Fonte: SPIE Publicador: SPIE
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
67.75915%
We present algorithms for automatic image annotation and retrieval based on color, shape, texture, and any combination of two or more of these features. Pixel- or region (object)-based color; region-based shape; and block- or region-based texture features have been considered. Automatic region selection has been accomplished by integrating color and spatial edge features. Color, shape, and texture indexing may be knowledge based (using appropriate training sets) or by example. The multifeature integration algorithms are designed to: (i) offer the user a wide range of options and flexibilities in order to enhance the outcome of the search and retrieval operations, and (ii) provide a compromise between accuracy and computational complexity, and vice versa. We demonstrate the performance of the proposed algorithms on a variety of images.; RIT community members may access full-text via RIT Libraries licensed databases: http://library.rit.edu/databases/

Region-based shape matching for automatic image annotation and query-by-example

Saber, Eli; Tekalp, Murat
Fonte: Journal of visual communication and image representation, Elsevier Publicador: Journal of visual communication and image representation, Elsevier
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
57.509272%
We present a method for automatic image annotation and retrieval based on query-by-example by region-based shape matching. The proposed method consists of two parts: region selection and shape matching. In the first part, the image is partitioned into disjoint, connected regions with more-or-less uniform color, whose boundaries coincide with spatial edge locations. Each region or valid combinations of neighboring regions constitute “potential objects.” In the second part, the shape of each potential object is tested to determine whether it matches one from a set of given templates. To this effect, we propose a new shape matching method, which is translation-, rotation-, and isotropic scale-invariant, where the boundary of each potential object, as well as of each template, is represented by a B-spline. We, then, identify correspondences between the joint points of the B-splines of potential objects and templates by using a modal matching method. These correspondences are used to estimate the parameters of an affine mapping to register the object with the template. A proximity measure is then computed between the two contours based on the Hausdorff distance. We demonstrate the performance of the proposed method on a variety of images.; RIT community members may access full-text via RIT Libraries licensed databases: http://library.rit.edu/databases/

XML path based relevance model for automatic image annotation

Rege, Manjeet; Dong, Ming; Fotouhi, Farshad
Fonte: IEEE: Proc. of IEEE International Conference on Multimedia and Expo Publicador: IEEE: Proc. of IEEE International Conference on Multimedia and Expo
Tipo: Proceedings
Português
Relevância na Pesquisa
47.90406%
This is the first paper that proposes automatic image annotation using the semantics of XML. In this paper, we propose XPRM - XML Path based Relevance Model for automatic image annotation. Our experimental results show that the proposed model has considerable advantage over single word annotations in performing automatic semantic annotation.; ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. …………………………………………………………………………………………………………………………………………………............................................. "XML Path based Relevance Model for Automatic Image Annotation", Proc. of IEEE International Conference on Multimedia and Expo. Held in Amsterdam, Netherlands: 6-8 July 2005

Semantic Cohesion for Image Annotation and Retrieval

Escalante,Hugo Jair; Sucar,Luis Enrique; Montes-y-Gómez,Manuel
Fonte: Centro de Investigación en computación, IPN Publicador: Centro de Investigación en computación, IPN
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/03/2012 Português
Relevância na Pesquisa
67.75915%
We present methods for image annotation and retrieval based on semantic cohesion among terms. On the one hand, we propose a region labeling technique that assigns an image the label that maximizes an estimate of semantic cohesion among candidate labels associated to regions in segmented images. On the other hand, we propose document representation techniques based on semantic cohesion among multimodal terms that compose images. We report experimental results that show the effectiveness of the proposed techniques. Additionally, we describe an extension of a benchmark collection for evaluation of the proposed techniques.