CLIMS — A System for Image Retrieval by Using Colour and Wavelet Features
Abstract
In this paper a system called CLIMS (CLausthal Image Management System) for content based image retrieval as an important subsystem of a general multimedia database is presented. It offers querying
by sketch and image example and uses colour and wavelet based features for the comparison of images. Each image in the database
is represented by a set of wavelet coefficients and colour attributes, which form the fundament for the retrieval.
In order to enable efficient similarity search two index structures, VP-Trees and Lq metric, are introduced and discussed. With the extension of the original VP-tree algorithm a ranking of the n most similar images is possible. The efficiency of the proposed retrieval methods is evaluated on a sample, general image
catalogue.
... Interfaces for the query of an image database include visual methods like query-by-pictorial-example (QBPE), query-by-painting/sketching or standard methods like browsing in a given set of sample images [5,7]. ...
... The query starts with the application of the wavelet transformation on the sample image. The result is a set of wavelet coefficients (c 0 , c 1 , ?, c 63 ) describing the image content (see [7] for a detailed description of this approach). Subsequently the Euclidian differences between the vector of the query image and the corresponding vectors of all images in the databases are calculated. ...
This paper presents an overview over parallel architectures for the efficient realisation of digital libraries by considering image databases as an example. The state of the art approach for image retrieval uses a priori extracted features and limits the applicability of the retrieval techniques, as a detail search for objects and for other important elements can't be performed. Well-suited algorithms for dynamic feature extraction and comparison are not often applied, as they require huge computational and memory resources. Integration of parallel methods and architectures enables the use of these alternative approaches for improved classification and retrieval of documents in digital libraries. Therefore implemented prototypes on a symmetric multiprocessor (SMP) and on cluster architecture are introduced in the paper. Performance measurements with a wavelet-based template matching method resulted into a reasonable speedup.
... Interfaces for the query of an image database include visual methods like query-by-pictorial-example (QBPE), query-by-painting/sketching or standard methods like browsing in a given set of sample images [5,7]. ...
... The query starts with the application of the wavelet transformation on the sample image. The result is a set of wavelet coefficients (c 0 , c 1 , …, c 63 ) describing the image content (see [7] for a detailed description of this approach). Subsequently the Euclidian differences between the vector of the query image and the corresponding vectors of all images in the databases are calculated. ...
: This paper presents an overview over parallel architectures for the efficient realisation of digital libraries by considering image databases as an example. The state of the art approach for image retrieval uses a priori extracted features and limits the applicability of the retrieval techniques, as a detail search for objects and for other important elements can't be performed. Well-suited algorithms for dynamic feature extraction and comparison are not often applied, as they require huge computational and memory resources. Integration of parallel methods and architectures enables the use of these alternative approaches for improved classification and retrieval of documents in digital libraries. Therefore implemented prototypes on a symmetric multiprocessor (SMP) and on cluster architecture are introduced in the paper. Performance measurements with a wavelet-based template matching method resulted into a reasonable speedup. 1.
... Interfaces for the query of an image database include visual methods like query-by-pictorialexample (QBPE), query-by-painting/sketching or standard methods like browsing in a given set of sample images [3,4]. ...
This chapter presents an introduction to the area of parallel and distributed multimedia database systems. The first part describes the characteristics of multimedia data and depicts the storage and annotation of such data in conventional and in multimedia databases. The main aim is to explain the process of multimedia retrieval by using images as an example. The related computational, storage, and network requirements create an urgent need for the integration of parallel and distributed computer architectures in modern multimedia information systems. Different hardware and software aspects have to be examined, for example the partitioning of multimedia data and the distribution over multiple nodes have a decisive impact on the performance, efficiency, and the usability of such multimedia databases. Other distributed aspects such as streaming techniques, proxy and client issues, security, etc. are only briefly mentioned and are not in the focus of this chapter. The last section gives an overview over an existing cluster-based prototype for image retrieval named Cairo.
In this paper, we propose a parallel computing technique for content-based image retrieval (CBIR) system. This technique is
mainly used for single node with multi-core processor, which is different from those based on cluster or network computing
architecture. Due to its specific applications (such as medical image processing) and the harsh terms of hardware resource
requirement, the CBIR system has been prevented from being widely used. With the increasing volume of the image database,
the widespread use of multi-core processors, and the requirement of the retrieval accuracy and speed, we need to achieve a
retrieval strategy which is based on multi-core processor to make the retrieval faster and more convenient than before. Experimental
results demonstrate that this parallel architecture can significantly improve the performance of retrieval system. In addition,
we also propose an efficient parallel technique with the combinations of the cluster and the multi-core techniques, which
is supposed to gear to the new trend of the cloud computing.
Keywordscontent-based image retrieval (CBIR)-parallel computing-shared-memory-feature extraction-similarity comparison
This article gives an overview of different approaches proposed for the storage and manipulation of clusters of images. Clustering images consists of grouping together images having a defined relationship. In this article, images are represented by quadtrees implemented in a hierarchical or linear way. The discussion, presented at the end of the article, allows selecting a quadtree-based representation well adapted to a specific area of application or to the characteristics of the manipulated images. oui
Systems for the archival and retrieval of images are used in many areas, for example medical applications, news agencies, etc. The state-of-the-art approach for image description considers a priori extracted features. The disadvantageous reduction of the image content onto a few low-level features limits the applicability of image databases. A search for objects and other important image components requires dynamic feature extraction. The related computational and storage requirements exceed the possibilities of computer architectures with a single processing element. Therefore we developed a cluster platform, which supports the implementation of this novel retrieval approach in existing systems. We introduce the basic principles of image retrieval with dynamic feature extraction and a cluster platform. The main focus regards thereby the workload balancing across the cluster. For this purpose we developed a scheduling heuristic and executed performance measurements with the implemented prototype. The obtained results are discussed
In this article the use of shape features in content-based image
retrieval is studied. The emphasis is on techniques which do not demand
object segmentation. PicSOM, the image retrieval system used in the
experiments, requires that features are represented by constant-sized
feature vectors for which the Euclidean distance can be used as a
similarity measure. The shape features suggested here are edge
histograms and Fourier transform based features computed for an edge
image in Cartesian and polar coordinate planes. The results show that
both local and global shape features are important clues of shapes in an
image
Throughout the academic year 1986-87, the University of Illinois was host to a symposium on mathematical analysis which was attended by some of the leading figures in the field. This book arises out of this special year and lays emphasis on the synthesis of modern and classical analysis at the current frontiers of knowledge. The contributed articles by the participants cover the gamut of mainstream topics. This book will be essential to researchers in mathematical analysis.
A class of algorithms is introduced for the rapid numerical application of a class of linear operators to arbitrary vectors. Previously published schemes of this type utilize detailed analytical information about the operators being applied and are specific to extremely narrow classes of matrices. In contrast, the methods presented here are based on the recently developed theory of wavelets and are applicable to all Calderon-Zygmund and pseudo-differential operators. The algorithms of this paper require order O(N) or O(N log N) operations to apply an N × N matrix to a vector (depending on the particular operator and the version of the algorithm being used), and our numerical experiments indicate that many previously intractable problems become manageable with the techniques presented here.
We present several algorithms suitable for analysis of broadcast video. First, we show how wavelet analysis of frames of
video can be used to detect transitions between shots in a video stream, thereby dividing the stream into segments. Next we
describe how each segment can be inserted into a video database using an indexing scheme that involves a wavelet-based “signature.”
Finally, we show that during a subsequent broadcast of a similar or identical video clip, the segment can be found in the
database by quickly searching for the relevant signature. The method is robust against noise and typical variations in the
video stream, even global changes in brightness that can fool histogram-based techniques. In the paper, we compare experimentally
our shot transition mechanism to a color histogram implementation, and also evaluate the effectiveness of our database-searching
scheme. Our algorithms are very efficient and run in realtime on a desktop computer. We describe how this technology could
be employed to construct a “smart VCR” that was capable of alerting the viewer to the beginning of a specific program or identifying
Advances in technologies for scanning, networking, and CD-ROM, lower prices for large disk storage, and acceptance of common image compression and file formats have contributed to an increase in the number, size, and uses of on-line image collections. New tools are needed to help users create, manage, and retrieve images from these collections. We are developing QBIC (query by image content), a prototype system that allows a user to create and query image databases in which the image content -- the colors, textures, shapes, and layout of images and the objects they contain -- is used as the basis of queries. This paper describes two sets of algorithms in QBIC. The first are methods that allow `query by color drawing,' a form of query in which a user draws an approximate color version of an image, and similar images are retrieved. These are automatic algorithms in the sense that no user action is necessary during database population. Secondly, we describe algorithms for semi-automatic identification of image objects during database population, improving the speed and usability of this manually-intensive step. Once outlined, detailed queries on the content-properties of these individual objects can be made at query time.
We construct orthonormal bases of compactly supported wavelets, with arbitrarily high regularity. The order of regularity increases linearly with the support width. We start by reviewing the concept of multiresolution analysis as well as several algorithms in vision decomposition and reconstruction. The construction then follows from a synthesis of these different approaches.
In image database systems, indexing facilitates the speed of data access because it replaces raw image manipulation with index processing and also because it reduces the run-time search effort. We formulate the content-based image indexing problem as a multi-dimensional nearest-neighbor search problem, and develop an optimistic vantagepoint tree algorithm that can dynamically adapt the indexed search process to the characteristics of given queries. Based on our performance study, the system typically only needs to touch less than twenty percent of the index entries for well-behaved queries, i.e., when the query images are relatively close to their nearest neighbors in the database. We have also carried out extensive performance experiments and report in this paper the results of these studies, which characterize the impacts of various configuration and workload parameters on the performance of the proposed algorithm.