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27
ASSERT: A Physician-in-the-loop Content-Based Retrieval System for HRCT Image Databases
, 1999
"... It is now recognized in many domains that content-based image retrieval (CBIR) from a database of images cannot be carried out by using completely automated approaches. One such domain is medical radiology for which the clinically useful information in an image typically consists of gray level varia ..."
Abstract
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Cited by 84 (8 self)
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It is now recognized in many domains that content-based image retrieval (CBIR) from a database of images cannot be carried out by using completely automated approaches. One such domain is medical radiology for which the clinically useful information in an image typically consists of gray level variations in highly localized regions of the image. Currently, it is not possible to extract these regions by automatic image segmentation techniques. To address this problem, we have implemented a human-in-the-loop (a physician-in-the-loop, more specifically) approach in which the human delineates the pathology bearing regions (PBR) and a set of anatomical landmarks in the image when the image is entered into the database. From the regions thus marked, our approach applies low-level computer vision and image processing algorithms to extract attributes related to the variations in gray scale, texture, shape, etc. In addition, the system records attributes that capture relational information such...
Interactive Content-Based Image Retrieval Using Relevance Feedback
, 2002
"... Database search engines are generally used in a one-shot fashion in which a user provides query information to the system and, in return, the system provides a number of database instances to the user. A relevance feedback system allows the user to indicate to the system which of these instances are ..."
Abstract
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Cited by 22 (1 self)
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Database search engines are generally used in a one-shot fashion in which a user provides query information to the system and, in return, the system provides a number of database instances to the user. A relevance feedback system allows the user to indicate to the system which of these instances are desirable, or relevant, and which are not. Based on this feedback, the system modifies its retrieval mechanism in an attempt to return a more desirable instance set to the user. In this paper, we present a relevance feedback technique that uses decision trees to learn a common thread among instances marked relevant. We apply our technique in a preexisting content-based image retrieval (CBIR) system that is used to access high resolution computed tomographic images of the human lung. We compare our approach to a commonly used relevance feedback technique for CBIR, which modifies the weights of a K nearest neighbor retriever. The results show that our approach achieves better retrieval as measured in off-line experiments and as judged by a radiologist who is a lung specialist.
Combination of Tangent Vectors and Local Representations for Handwritten Digit Recognition
, 2002
"... Statistical classification using tangent vectors and classification based on local features are two successful methods for various image recognition problems. These two approaches tolerate global and local transformations of the images, respectively. Tangent vectors can be used to obtain global inva ..."
Abstract
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Cited by 17 (6 self)
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Statistical classification using tangent vectors and classification based on local features are two successful methods for various image recognition problems. These two approaches tolerate global and local transformations of the images, respectively. Tangent vectors can be used to obtain global invariance with respect to small affine transformations and line thickness, for example. On the other hand, a classifier based on local representations admits the distortion of parts of the image.
Use of Shape Features in Content-Based Image Retrieval
, 1999
"... The aim of the thesis was to study the use of shape features in content-based image retrieval and to implement shape features to such a system called PicSOM. Because of the internal structure and principles of PicSOM the emphasis was put on such shape feature techniques which can be represented by c ..."
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Cited by 17 (4 self)
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The aim of the thesis was to study the use of shape features in content-based image retrieval and to implement shape features to such a system called PicSOM. Because of the internal structure and principles of PicSOM the emphasis was put on such shape feature techniques which can be represented by constant-sized feature vectors and with which the Euclidean distance can be used as a similarity measure. The review covers the best-known shape description techniques which have been published in scientic journals and conference proceedings. Based on the review, some techniques were selected for the...
A Universal Model for Content-Based Image Retrieval
"... Abstract—In this paper a novel approach for generalized image retrieval based on semantic contents is presented. A combination of three feature extraction methods namely color, texture, and edge histogram descriptor. There is a provision to add new features in future for better retrieval efficiency. ..."
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Cited by 16 (0 self)
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Abstract—In this paper a novel approach for generalized image retrieval based on semantic contents is presented. A combination of three feature extraction methods namely color, texture, and edge histogram descriptor. There is a provision to add new features in future for better retrieval efficiency. Any combination of these methods, which is more appropriate for the application, can be used for retrieval. This is provided through User Interface (UI) in the form of relevance feedback. The image properties analyzed in this work are by using computer vision and image processing algorithms. For color the histogram of images are computed, for texture cooccurrence matrix based entropy, energy, etc, are calculated and for edge density it is Edge Histogram Descriptor (EHD) that is found. For retrieval of images, a novel idea is developed based on greedy strategy to reduce the computational complexity. The entire system was developed using AForge.Imaging (an open source product), MATLAB.NET Builder, C#, and Oracle 10g. The system was tested with Coral Image database containing 1000 natural images and achieved better results.
Bayesian Representations and Learning Mechanisms for Content-Based Image Retrieval
- in SPIE Storage and Retrieval for Media Databases 2000
, 2000
"... We have previously introduced a Bayesian framework for content-based image retrieval (CBIR) that relies on a generative model for feature representation based on embedded mixtures. This is a truly generic image representation that can jointly model color and texture and has been shown to perform wel ..."
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Cited by 13 (0 self)
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We have previously introduced a Bayesian framework for content-based image retrieval (CBIR) that relies on a generative model for feature representation based on embedded mixtures. This is a truly generic image representation that can jointly model color and texture and has been shown to perform well across a broad spectrum of image databases. In this paper, we expand the Bayesian framework along two directions. First, we show that the formulation of CBIR as a problem of Bayesian inference leads to a natural criteria for evaluating local image similarity without requiring any image segmentation. This allows the practical implementation of retrieval systems where users can provide image regions, or objects, as queries. Region-based queries are significantly less ambiguous than queries based on entire images leading to significant improvements in retrieval precision. Second, we present a Bayesian learning algorithm that relies on belief propagation to integrate feedback provided by the...
Local Representations for Multi-Object Recognition
- In Proc. of Deutsche Arbeitsgemeinschaft für Mustererkennung: DAGM 2003, 2003
, 2003
"... Methods for the recognition of multiple objects in images using local representations are introduced. Starting from a straight forward approach, we combine the use of local representations with region segmentation and template matching. The performance of the classifiers is evaluated on four image d ..."
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Cited by 9 (1 self)
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Methods for the recognition of multiple objects in images using local representations are introduced. Starting from a straight forward approach, we combine the use of local representations with region segmentation and template matching. The performance of the classifiers is evaluated on four image databases of different difficulties. All databases consist of images containing one, two or three objects and differ in the backgrounds which are used. Also, the presence or absence of occlusions of the objects in the scenes is considered. Classification results are promising regarding the difficulty of the task.
A survey on: Contents based search in image databases
- Journal of Computational and Graphical Statistics
, 2000
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Image segmentation in content-based image retrieval
, 2002
"... In recent years visual information has become increasingly important. Vast numbers of digital images are being produced nowadays. In order to utilise the images, they must be organised into databases, from which they can be searched based on various criteria. Content-based image retrieval (CBIR) sys ..."
Abstract
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Cited by 8 (2 self)
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In recent years visual information has become increasingly important. Vast numbers of digital images are being produced nowadays. In order to utilise the images, they must be organised into databases, from which they can be searched based on various criteria. Content-based image retrieval (CBIR) systems have become a popular subject of research recently. In CBIR retrieval is based on visual image features, which can be extracted automatically from the images without the need for human intervention or interpretation. However, a good way of characterising the visual content of images is difficult to come up with. Thus, instead of a perfect solution CBIR systems must be able to make use of a partial solution to the problem of image understanding when characterising images. The literature survey part of this work is divided into two sections. The first section covers issues related to the implementation of a content-based image retrieval system on general level. The second section discusses how segmentation of images into visually homogeneous regions can be utilised when characterising images and assessing the similarity of two images. Examples of the use of segmentation in several existing CBIR systems are also given.
The customized-queries approach to CBIR
, 1999
"... This paper introduces a new approach called the "customized-queries" approach to content-based image retrieval (CBIR). The customized-queries approach first classifies a query using the features that best differentiate the major classes and then customizes the query to that class by using ..."
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Cited by 7 (1 self)
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This paper introduces a new approach called the "customized-queries" approach to content-based image retrieval (CBIR). The customized-queries approach first classifies a query using the features that best differentiate the major classes and then customizes the query to that class by using the features that best distinguish the subclasses within the chosen major class. This research is motivated by the observation that the features that are most effective in discriminating among images from different classes may not be the most effective for retrieval of visually similar images within a class. This occurs for domains in which not all pairs of images within one class have equivalent visual similarity. We apply this approach to content-based retrieval of high-resolution tomographic images of patients with lung disease and show that this approach yields 82.8% retrieval precision. The traditional approach that performs retrieval using a single feature vector yields only 37.9% retrieval precision.
