Browsing by Subject "Content based retrieval"

Now showing 1 - 12 of 12

Open Access
Automatic image captioning
(2004) Pan J.-Y.; Yang H.-J.; Duygulu, Pınar; Faloutsos, C.
In this paper, we examine the problem of automatic image captioning. Given a training set of captioned images, we want to discover correlations between image features and keywords, so that we can automatically find good keywords for a new image. We experiment thoroughly with multiple design alternatives on large datasets of various content styles, and our proposed methods achieve up to a 45% relative improvement on captioning accuracy over the state of the art.
Open Access
Database research at Bilkent University
(ACM, 2005) Ulusoy, Özgür
The research activities of the Database Research Group of Bilkent University are discussed. The research is mainly focused on the topics of multimedia databases, Web databases, and mobile computing. The Ottoman Archive Content-Based Retrieval system is a Web-based program that provides electronic access to digitally stored Ottoman document images. The issues involved in adding a native score management system to object-relational databases, to be used in querying web metadata are also discussed.
Open Access
Haber videolarında ilgililik geribeslemesiyle içerik tabanlı erişim
(IEEE, 2006-04) Çavuş, Özge; Aksoy, Selim
Content-based retrieval in news video databases has become an important task with the availability of large quantities of data in both public and proprietary archives. We describe a relevance feedback technique that captures the significance of different features at different spatial locations in an image. Spatial content is modeled by partitioning images into non-overlapping grid cells. Contributions of different features at different locations are modeled using weights defined for each feature in each grid cell. These weights are iteratively updated based on user's feedback in terms of positive and negative labeling of retrieval results. Given this labeling, the weight updating scheme uses the ratios of standard deviations of the distances between relevant and irrelevant images to the standard deviations of the distances between relevant images. The proposed technique is quantitatively and qualitatively evaluated using shots related to several sports from the news video collection of the TRECVID video retrieval evaluation where the weights could capture relative contributions of different features and spatial locations. © 2006 IEEE.
Open Access
How content management problem of a remote laboratory system can be handled by integrating an open source learning management system? Problems and solutions
(IEEE, 2007) Özdoğru, Burcu; Cagıltay, N. E.
This paper represents the design and implementation of the integration process of an open source learning management system (LMS) to the remote laboratory platform. The reason of using a learning management system is to prevent the problems which can be seen in the learner side of the remote laboratory systems. However, since using a learning management system such as Moodle can handle the learner problems, since it is a separate system, there are still other problems to be handled such as integrating it with the other parts of remote laboratory systems. This study explores the background information of LMS, the problems faced while integrating Moodle to remote laboratory applications, the chosen content management systems' architecture, our architecture for integrating Moodle with the remote laboratory system and the solutions we propose for the problems.
Open Access
İçerik tabanlı görüntü erişimi için sahne sınıflandırması
(IEEE, 2008-04) Çavuş, Özge; Aksoy, Selim
Son yıllarda çok geniş veri tabanlarının kullanımıyla birlikte içerik tabanlı görüntü indekslemesi ve erişimi önemli bir araştırma konusu halini almıştır. Bu çalışmada, görüntü indekslemesi için sahne sınıflandırmasını baz alan bir görüntü erişim sistemi tanımlanmıştır. Görüntülerden çıkarılan alt düzey öznitelikler görüntü indekslemesinde doğrudan kullanılmak yerine, bu öznitelikler sahne sınıflandırması için kullanılmış ve görüntüler sınıflandırma sonucunda elde edilen anlamsal sınıf bilgileriyle indekslenmiştir. Sahne sınıflandırması için “kelime kümesi” (bag of words) dokuman analizi yöntemi olarak bilinen tekniğin bir uyarlaması kullanılmıştır. Görüntü erişim sistemini insan algısıyla desteklemek ve anlambilimsel uçurumu en aza indirgemek için erişim senaryosuna tek sınıf sınıflandırıcı bazlı ilgililik geri beslemesi eklenmiştir. Bunun için, ilgili görüntüleri çok iyi modelleyen, ilgili olmayan görüntülerden de bir o kadar uzak duran bir hiperkure oluşturan destek vektör veri tanımlaması kullanılmıştır. Önerilen yöntemler Corel veri kümesinde denenmiş ve başarılı sonuçlar elde edilmiştir. Content-based image indexing and retrieval have become important research problems with the use of large databases in a wide range of areas. In this study, a content-based image retrieval system that is based on scene classification for image indexing is proposed. Instead of using low-level features directly, semantic class information that is obtained as a result of scene classification is used during indexing. The traditional "bag of words" approach is modified for classifying the scenes. In order to minimize the semantic gap, a relevance feedback approach that is based on one-class classification is also integrated. The support vector data description is used for learning during feedback iterations. The experiments using the Corel data set show good results for both classification and retrieval. ©2008 IEEE.
Open Access
Learning bayesian classifiers for scene classification with a visual grammar
(IEEE, 2005-03) Aksoy, Selim; Koperski, K.; Tusk, C.; Marchisio, G.; Tilton, J. C.
A challenging problem in image content extraction and classification is building a system that automatically learns high-level semantic interpretations of images. We describe a Bayesian framework for a visual grammar that aims to reduce the gap between low-level features and high-level user semantics. Our approach includes modeling image pixels using automatic fusion of their spectral, textural, and other ancillary attributes; segmentation of image regions using an iterative split-and-merge algorithm; and representing scenes by decomposing them into prototype regions and modeling the interactions between these regions in terms of their spatial relationships. Naive Bayes classifiers are used in the learning of models for region segmentation and classification using positive and negative examples for user-defined semantic land cover labels. The system also automatically learns representative region groups that can distinguish different scenes and builds visual grammar models. Experiments using Landsat scenes show that the visual grammar enables creation of high-level classes that cannot be modeled by individual pixels or regions. Furthermore, learning of the classifiers requires only a few training examples.
Open Access
Mağaza katalogları içerisinde resim arama
(IEEE, 2009-04) Baysal, Sermetcan; Kurt, Mehmet Can; Aydoğdu, Gonca; Damcı, Pelin; Telmen, İlay; Duygulu, Pınar
In this paper, an overview of an application, which aims to make significant improvements on access methods to the online shopping catalogs, is presented. In current online shopping sites, only browsing and semantic based retrieval are provided to the users. In this work, a system is constructed on content based retrieval methods in order to allow users to find a clothing item that they are searching within the online catalogs. The results have came out to be impressive when they are examined by the human eye. This work makes use of existing computer vision techniques and applies them to the area of clothing and shopping to provide users with a useful application. © 2009 IEEE.
Open Access
Modeling of remote sensing image content using attributed relational graphs
(Springer, 2006-08) Aksoy, Selim
Automatic content modeling and retrieval in remote sensing image databases are important and challenging problems. Statistical pattern recognition and computer vision algorithms concentrate on feature-based analysis and representations in pixel or region levels whereas syntactic and structural techniques focus on modeling symbolic representations for interpreting scenes. We describe a hybrid hierarchical approach for image content modeling and retrieval. First, scenes are decomposed into regions using pixel-based classifiers and an iterative split-and-merge algorithm. Next, spatial relationships of regions are computed using boundary, distance and orientation information based on different region representations. Finally, scenes are modeled using attributed relational graphs that combine region class information and spatial arrangements. We demonstrate the effectiveness of this approach in query scenarios that cannot be expressed by traditional approaches but where the proposed models can capture both feature and spatial characteristics of scenes and can retrieve similar areas according to their high-level semantic content. © Springer-Verlag Berlin Heidelberg 2006.
Open Access
A new approach to search result clustering and labeling
(Springer, Berlin, Heidelberg, 2011) Türel, Anıl; Can, Fazlı
Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of retrieval results for easier access of desired information is an important research problem. In this paper, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient-based and sequential k-means clustering algorithms. A cluster labeling method based on term weighting is also introduced for reflecting cluster contents. In addition, we present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative strategy to derive the relative performance of the proposed method with respect to two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Experimental results in the publicly available AMBIENT and ODP-239 datasets show that our method can successfully achieve both clustering and labeling tasks. © 2011 Springer-Verlag Berlin Heidelberg.
Open Access
Osmanlı arşivleri içerik-bazlı sorgulama (İBS) sistemi
(2006-04) Altıngövde, İsmail Şengör; Şaykol, Ediz; Ulusoy, Özgür; Güdükbay, Uğur; Çetin, A. Enis; Göçmen, M.
We propose a content-based retrieval (CBR) system for digital Ottoman archive documents. In this system, the symbols extracted from the documents are matched with the most similar one in the symbol library, which is created in a supervised manner. The users specify queries by marking a region on an example document and the system retrieves all documents that include the symbols found in the query region. A prototype of the system is currently available on the Web. © 2006 IEEE.
Open Access
A relevance feedback technique for multimodal retrieval of news videos
(IEEE, 2005-11) Aksoy, Selim; Çavuş Özge
Content-based retrieval in news video databases has become an important task with the availability of large quantities of data in both public and proprietary archives. We describe a relevance feedback technique that captures the significance of different features at different spatial locations in an image. Spatial content is modeled by partitioning images into non-overlapping grid cells. Contributions of different features at different locations are modeled using weights defined for each feature in each grid cell. These weights are iteratively updated based on user's feedback in terms of positive and negative labeling of retrieval results. Given this labeling, the weight updating scheme uses the ratios of standard deviations of the distances between relevant and irrelevant images to the standard deviations of the distances between relevant images. The proposed technique is quantitatively and qualitatively evaluated using shots related to several sports from the news video collection of the TRECVID video retrieval evaluation where the weights could capture relative contributions of different features and spatial locations. © 2005 IEEE.
Open Access
Retrieval of Ottoman documents
(ACM, 2006-10) Ataer, Esra; Duygulu, Pınar
There is a growing need to access historical Ottoman documents stored in large archives and therefore managing tools for automatic searching, indexing and transcription of these documents is required. In this paper, we present a method for the retrieval of Ottoman documents based on word matching. The method first successfully segments the documents into word images and then uses a hierarchical matching technique to find the similar instances of the word images. The experiments show that even with simple features promising results can be achieved. Copyright 2006 ACM.