Josiane M. Bueno

Efficient Content-Based Image Retrieval through Metric Histograms

This paper presents a new and efficient method for content-based image retrieval employing the color distribution of images. This new method, called metric histogram, takes advantage of the correlation among adjacent bins of histograms, reducing the dimensionality of the feature vectors extracted from images, leading to faster and more flexible indexing and retrieval processes. The proposed technique works on each image independently from the others in the dataset, therefore there is no pre-defined number of color regions in the resulting histogram. Thus, it is not possible to use traditional comparison algorithms such as Euclidean or Manhattan distances. To allow the comparison of images through the new feature vectors given by metric histograms, a new metric distance function MHD( ) is also proposed. This paper shows the improvements in timing and retrieval discrimination obtained using metric histograms over traditional ones, even when using images with different spatial resolution or thumbnails. The experimental evaluation of the new method, for answering similarity queries over two representative image databases, shows that the metric histograms surpass the retrieval ability of traditional histograms because they are invariant on geometrical and brightness image transformations, and answer the queries up to 10 times faster than the traditional ones.

How to add content-based image retrieval capability in a PACS

This paper presents a new picture archiving and communication system (PACS), called cbPACS (content-based PACS), which has content-based image retrieval resources. cbPACS answers similarity (range and nearest-neighbor) queries, taking advantage of a metric access method embedded into the image database manager. The images are compared via their features, which are extracted by an image processing system module. The system works on features based on the color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant with regard to scale, translation and rotation of images and also to brightness transformations. cbPACS is prepared to integrate new image features, based on the texture and shape of the main objects in the image.

Using an image-extended relational database to support content-based image retrieval in a PACS

This paper presents a new Picture Archiving and Communication System (PACS), called cbPACS, which has content-based image retrieval capabilities. The cbPACS answers range and k-nearest- neighbor similarity queries, employing a relational database manager extended to support images. The images are compared through their features, which are extracted by an image-processing module and stored in the extended relational database. The database extensions were developed aiming at efficiently answering similarity queries by taking advantage of specialized indexing methods. The main concept supporting the extensions is the definition, inside the relational manager, of distance functions based on features extracted from the images. An extension to the SQL language enables the construction of an interpreter that intercepts the extended commands and translates them to standard SQL, allowing any relational database server to be used. By now, the system implemented works on features based on color distribution of the images through normalized histograms as well as metric histograms. Metric histograms are invariant regarding scale, translation and rotation of images and also to brightness transformations. The cbPACS is prepared to integrate new image features, based on texture and shape of the main objects in the image.

The Metric Histogram: A New and Efficient Approach for Content-based Image Retrieval

This paper presents the metric histogram, a new and efficient technique to capture the brightness feature of images, allowing faster retrieval of images based on their content. Histograms provide a fast way to chop down large subsets of images, but are difficult to be indexed in existing data access methods. The proposed metric histograms reduce the dimensionality of the feature vectors leading to faster and more flexible indexing and retrieval processes. A new metric distance function DM( ) to measure the dissimilarity between images through their metric histograms is also presented. This paper shows the improvements obtained using the metric histograms over the traditional ones, through experiments for answering similarity queries over two databases containing respectively 500 and 4,247 magnetic resonance medical images. The experiments performed showed that metric histograms are more than 10 times faster than the traditional approach of using histograms and keep the same recovering capacity.

Extending relational databases to support content-based retrieval of medical images

This paper shows how to support images in a relational database, so it can fulfill the requirements to be used as the storage mechanism of a PACS. This support includes the ability to answer similarity queries based on the image content, providing fast image retrieval based on indexing structures. The main concept allowing this support is the definition of distance functions based on features, which are extracted from the images as they are stored in the database. An extension to SQL enables the construction of an interpreter that intercepts the extended commands and translates them into standard SQL, allowing one to take advantage of any relational database server. We describe experiments made with a prototype implemented using these concepts, which allowed answering queries up to 20 times faster than using existing relational servers alone.

3D Reconstruction of Tomographic Images Applied to Largely Spaced Slices

This paper presents a full reconstruction process of magnetic resonance images. The first step is to bring the acquired data from the frequency domain, using a Fast Fourier Transform algorithm. A Tomographic Image Interpolation is then used to transform a sequence of tomographic slices in an isotropic volume data set, a process also called 3D Reconstruction. This work describes an automatic method whose interpolation stage is based on a previous matching stage using Delaunay Triangulation. The reconstruction approach uses an extrapolation procedure that permits appropriate treatment of the boundaries of the object under analysis.

3D reconstruction of magnetic resonance imaging using largely spaced slices

This paper presents a full reconstruction process of magnetic resonance images. The first step is to bring the acquired data from the frequency domain, using a fast Fourier transform algorithm. A tomographic image interpolation is then used to transform a sequence of tomographic slices in an isotropic volume data set, a process also called 3D reconstruction. This work describes a method the interpolation stage of which is based on a previous matching stage using Delaunay triangulation.

Model for Automatic Text Classification and Categorization for Image Indexing and Retrieval

A class of complementary quadriphase sequence based on Jacket matrix was proposed in this paper. It is with zero cross correlation zone and near optimal autocorrelation to efficiently eliminate the interuser interferences for DS-CDMA mobile communication systems. Unlike the conventional designs, the proposed sequences can be easily extended and generated to large odd and even sizes by using a fast linear Hadamard transform. The computer simulations also show that the proposed sequences have better performance than conventional multiuser spreading CDMA systems.Traditional Content-based Information Retrieval Sys- tems (CBIR) use low level characteristics, this is, primary characteristics such as the color, shape, texture and also in textual attributes related to images. Although, users make queries based on semantics, which are not representatives just by such low level characteristics. Recent works on content-based image retrieval have demonstrated that re- searchers have been trying to map visual low level charac- teristics and high level semantics. These work have moti- vated this paper which proposes a model for automatic text classification and categorization for image searching by us- ing an self-organizing neural network architecture. Experi- mental results confirm this text-based model is complemen- tary to image-driven techniques such as Retin.