Fabio Jun Takada Chino

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.

DBM-Tree: trading height-balancing for performance in metric access methods

Metric Access Methods (MAM) are employed to accelerate the processing of similarity queries, such as the range and the k-nearest neighbor queries. Current methods, such as the Slim-tree and the M-tree, improve the query performance minimizing the number of disk accesses, keeping a constant height of the structures stored on disks (height-balanced trees). However, the overlapping between their nodes has a very high influence on their performance. This paper presents a new dynamic MAM called theDBM-tree (Density-Based Metric tree), which can minimize the overlap between high-density nodes by relaxing the height-balancing of the structure. Thus, the height of the tree is larger in denser regions, in order to keep a tradeoff between breadth-searching and depth-searching. An underpinning for cost estimation on tree structures is their height, so we show a non-height dependable cost model that can be applied for DBM-tree. Moreover, an optimization algorithm calledShrink is also presented, which improves the performance of an already builtDBM-tree by reorganizing the elements among their nodes. Experiments performed over both synthetic and real world datasets showed that theDBM-tree is, in average, 50% faster than traditional MAM and reduces the number of distance calculations by up to 72% and disk accesses by up to 66%. After performing the Shrink algorithm, the performance improves up to 40% regarding the number of disk accesses for range andk-nearest neighbor queries. In addition, theDBM-tree scales up well, exhibiting linear performance with growing number of elements in the database.

MAMView: a visual tool for exploring and understanding metric access methods

The MAMView framework is a data exploration tool that allows developers and users of Metric Access Methods (MAMs) to explore and share dynamic and interactive 3D presentations of a MAM, making the understanding of those structures easier. It is able to create visual representations of metric datasets, including high-dimensional and non-dimensional information. This is achieved by using an extension of the FastMap algorithm. This framework was developed as a practical tool that has been successfully applied to study existing MAMs, helping both new and experienced users to better understand them. The MAMView was also applied to a new under development MAM. With MAMView in hands, the development team of this MAM was able to drill-down its algorithms, quickly finding problems and also potential points for improvement and optimizations. Our focus on this work is on proposing an intuitive yet powerful visualization framework that can be easily employed to build intuitive visual presentations that can bypass the drawback of MAMs dealing with datasets with no spatial representation. Besides MAMView being a powerful visualization tool, its greatest strengths are the ability to interactively explore a visual presentation of a MAM at any level of detail, and the ability to seamlessly query and produce graphical representations in XML format that can be straightforward executed. This paper presents the MAMView framework and its main techniques, describes the current tool, and reports on our experiences in applying it to real applications.

A visual framework to understand similarity queries and explore data in Metric Access Methods

This paper presents the MAMView framework to help users and developers in understanding the data organisation in Metric Access Methods (MAM). Users and developers can explore and share dynamic and interactively 2- or 3-dimensional representations of a MAM. Such representations can be the steps of a similarity query or the insertion of an object, or the data organisation in a MAM. MAMView was developed as a practical tool that has been successfully applied in studying existing MAM, helping novice users to better understand the behaviour and properties of such structures, as well developers to verify and drill-down their new proposed structures.