A content based image retrieval system for a biological specimen collection

Digital photography and decreasing cost of storing data in digital form has led to an explosion of large digital image repositories. Since the number of images in image databases can be large (millions in some cases) it is important to develop automated tools to search them. In this paper, we present a content based image retrieval system for a database of parasite specimen images. Unlike most content based image retrieval systems, where the database consists of objects that vary widely in shape and size, the objects in our database are fairly uniform. These objects are characterized by flexible body shapes, but with fairly rigid ends. We define such shapes to be FleBoRE (Flexible Body Rigid Extremities) objects, and present a shape model for this class of objects. We have defined similarity functions to compute the degree of likeness between two FleBoRE objects and developed automated methods to extract them from specimen images. The system has been tested with a collection of parasite images from the Harold W. Manter Laboratory for Parasitology. Empirical and expert-based evaluations show that query by shape approach is effective in retrieving specimens of the same class.     

Signature file as a spatial filter for iconic image database

The problems of efficient data storage and data retrieval are important issues in the design of image database systems. A data structure called a 2-D string, which represents symbolic pictures preserving spatial knowledge, was proposed by Chang et al. It allows a natural way to construct iconic indexes for pictures. We proposed a data structure 2-D B-string to characterize the spatial knowledge embedded in images. It is powerful enough to describe images with partly overlapping or completely overlapping objects without the need of partitioning objects. When there exist a large volume of complex images in the image database, the processing time for image retrieval is tremendous. It is essential to develop efficient access methods for retrieval. In this paper, access methods, to different extents of precision, for retrieval of desired images encoded in 2-D B-strings are proposed. The signature file acting as a spatial filter of image database is based on disjoint coding and superimposed coding techniques. It provides an efficient way to retrieve images in image databases.     

Similarity searching in medical image databases

We propose a method to handle approximate searching by image content in medical image databases. Image content is represented by attributed relational graphs holding features of objects and relationships between objects. The method relies on the assumption that a fixed number of “labeled” or “expected” objects (e.g., “heart”, “lungs”, etc.) are common in all images of a given application domain in addition to a variable number of “unexpected” or “unlabeled” objects (e.g., “tumor”, “hematoma”, etc.). The method can answer queries by example, such as “find all X-rays that are similar to Smith's X-ray”. The stored images are mapped to points in a multidimensional space and are indexed using state-of-the-art database methods (R-trees). The proposed method has several desirable properties: (a) Database search is approximate, so that all images up to a prespecified degree of similarity (tolerance) are retrieved. (b) It has no “false dismissals” (i.e., all images qualifying query selection criteria are retrieved). (c) It is much faster than sequential scanning for searching in the main memory and on the disk (i.e., by up to an order of magnitude), thus scaling-up well for large databases     

Structural feature indexing for retrieval of partially visible shapes

Efficient and robust information retrieval from large image databases is an essential functionality for the reuse, manipulation, and editing of multimedia documents. Structural feature indexing is a potential approach to efficient shape retrieval from large image databases, but the indexing is sensitive to noise, scales of observation, and local shape deformations. It has now been confirmed that efficiency of classification and robustness against noise and local shape transformations can be improved by the feature indexing approach incorporating shape feature generation techniques (Nishida, Comput. Vision Image Understanding 73 (1) (1999) 121-136). In this paper, based on this approach, an efficient, robust method is presented for retrieval of model shapes that have parts similar to the query shape presented to the image database. The effectiveness is confirmed by experimental trials with a large database of boundary contours obtained from real images, and is validated by systematically designed experiments with a large number of synthetic data.     

Indexing shapes in image databases using the centroid–radii model

In content-based image retrieval systems, the content of an image such as color, shapes and textures are used to retrieve images that are similar to a query image. Most of the existing work focus on the retrieval effectiveness of using content for retrieval, i.e., study the accuracy (in terms of recall and precision) of using different representations of content. In this paper, we address the issue of retrieval efficiency, i.e., study the speed of retrieval, since a slow system is not useful for large image databases. In particular, we look at using the shape feature as the content of an image, and employ the centroid–radii model to represent the shape feature of objects in an image. This facilitates multi-resolution and similarity retrievals. Furthermore, using the model, the shape of an object can be transformed into a point in a high-dimensional data space. We can thus employ any existing high-dimensional point index as an index to speed up the retrieval of images. We propose a multi-level R-tree index, called the Nested R-trees (NR-trees) and compare its performance with that of the R-tree. Our experimental study shows that NR-trees can reduce the retrieval time significantly compared to R-tree, and facilitate similarity retrieval. We note that our NR-trees can also be used to index high-dimensional point data commonly found in many other applications.     

Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data

Reverse nearest neighbor (RNN) search is very crucial in many real applications. In particular, given a database and a query object, an RNN query retrieves all the data objects in the database that have the query object as their nearest neighbors. Often, due to limitation of measurement devices, environmental disturbance, or characteristics of applications (for example, monitoring moving objects), data obtained from the real world are uncertain (imprecise). Therefore, previous approaches proposed for answering an RNN query over exact (precise) database cannot be directly applied to the uncertain scenario. In this paper, we re-define the RNN query in the context of uncertain databases, namely probabilistic reverse nearest neighbor (PRNN) query, which obtains data objects with probabilities of being RNNs greater than or equal to a user-specified threshold. Since the retrieval of a PRNN query requires accessing all the objects in the database, which is quite costly, we also propose an effective pruning method, called geometric pruning (GP), that significantly reduces the PRNN search space yet without introducing any false dismissals. Furthermore, we present an efficient PRNN query procedure that seamlessly integrates our pruning method. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed GP-based PRNN query processing approach, under various experimental settings.     

Fast Retrieval of Isolated Visual Shapes

Similarity-based retrieval from databases of isolated visual shapes has become an important information retrieval problem. The goal of the current work is to achieve high retrieval speed with reasonable retrieval effectiveness, and support for partial and occluded shape queries. In the proposed method, histograms of local shape parts are coded as index vectors. To increase retrieval accuracy, a rich set of parts at all scales of the shape is used; specifically, the parts are defined as connected sequences of regions in curvature scale space. To increase efficiency, structural indexing is used to compare the index vectors of the query and database shapes. In experimental evaluations, the method retrieved at least one similar shape in the top three retrieved items 99–100% of the time, depending on the database. Average retrieval times ranged from 0.7 ms on a 131-shape database to 7 ms on a 1310-shape database. The method is thus suitable for fast, approximate shape retrieval in comparison with more accurate but more costly structural matching.     

TQuery: a context-sensitive temporal query language.

Users of electronic medical databases request pertinent information by recasting their clinical questions into a formal database query language. Because the query language is the user's only access to the data, the query language must be powerful enough to enable users to express their data requirements. However, a competing need is for the query language to be restrictive enough so that queries can have unambiguous semantics and the query processor can generate correct answers. We describe a query language, called TQuery , that was designed specifically to formulate database queries that are dependent on temporal and contextual relationships. TQuery specifications express contextual constraints without the need to explicitly reference calendar dates. TQuery is the database query language used to retrieve patient data from an object-oriented electronic patient medical-record system called the temporal network (TNET). TNET and TQuery were developed to support the real-time temporal reasoning and representation needs of a LISP workstation-based medical expert system.     

Integrating symbolic images into a multimedia database system using classification and abstraction approaches

Symbolic images are composed of a finite set of symbols that have a semantic meaning. Examples of symbolic images include maps (where the semantic meaning of the symbols is given in the legend), engineering drawings, and floor plans. Two approaches for supporting queries on symbolic-image databases that are based on image content are studied. The classification approach preprocesses all symbolic images and attaches a semantic classification and an associated certainty factor to each object that it finds in the image. The abstraction approach describes each object in the symbolic image by using a vector consisting of the values of some of its features (e.g., shape, genus, etc.). The approaches differ in the way in which responses to queries are computed. In the classification approach, images are retrieved on the basis of whether or not they contain objects that have the same classification as the objects in the query. On the other hand, in the abstraction approach, retrieval is on the basis of similarity of feature vector values of these objects. Methods of integrating these two approaches into a relational multimedia database management system so that symbolic images can be stored and retrieved based on their content are described. Schema definitions and indices that support query specifications involving spatial as well as contextual constraints are presented. Spatial constraints may be based on both locational information (e.g., distance) and relational information (e.g., north of). Different strategies for image retrieval for a number of typical queries using these approaches are described. Estimated costs are derived for these strategies. Results are reported of a comparative study of the two approaches in terms of image insertion time, storage space, retrieval accuracy, and retrieval time. Received June 12, 1998 / Accepted October 13, 1998     

Retrieving 2D shapes using caterpillar decomposition

Graphs provide effective data structures modeling complex relations and schemaless data such as images, XML documents, circuits, compounds, and proteins. Given a query graph, finding sufficiently similar database graphs without performing a sequential search is an important problem arising in different domains. In this paper, we propose a new method for indexing tree structures based on a graph-theoretic concept called caterpillar decomposition. Our algorithm starts by representing each tree along with its subtrees in the geometric space using its caterpillar decomposition. After representing the query in the same fashion, similar database trees are retrieved efficiently by means of nearest neighbor searches. We have successfully evaluated the proposed algorithm on two shape databases and include a set of perturbation experiments that establish the algorithm’s robustness to noise. We have also shown that the approach compares favorably to previous approaches for shape retrieval on these datasets.     

A fully spatial personalized differentially private mechanism to provide non-uniform privacy guarantees for spatial databases

Spatial databases are essential to applications in a wide variety of domains. One of the main privacy concerns when answering statistical queries, such as range counting queries, over a spatial database is that an adversary observing changes in query answers may be able to determine whether or not a particular geometric object is present in the database. Differential privacy addresses this concern by guaranteeing that the presence or absence of a geometric object has little effect on query answers. Most of the current differentially private mechanisms for spatial databases ignore the fact that privacy is personal and, thus, provide the same privacy protection for all geometric objects. However, some particular geometric objects may be more sensitive to privacy issues than others, requiring stronger differential privacy guarantees. In this paper, we introduce the concept of spatial personalized differential privacy for spatial databases where different geometric objects have different privacy protection requirements. Also, we present SPDP-PCE, a novel spatial personalized differentially private mechanism to answer range counting queries over spatial databases that fully considers the privacy protection requirements of geometric objects in the underlying geometric space in both steps of noise addition and consistency enforcement. Our experimental results on real datasets demonstrate the effectiveness of SPDP-PCE under various total privacy budgets, query shapes, and privacy level distributions.     

Deformation tolerant generalized Hough transform for sketch-based image retrieval in complex scenes

Sketch-based image retrieval systems need to handle two main problems. First of all, they have to recognize shapes similar but not necessarily identical to the user’s query. Hence, exact object identification techniques do not fit in this case. The second problem is the selection of the image features to compare with the user’s sketch. In domain-independent visual repositories, real-life images with non-uniform background and possible occluding objects make this second task particularly hard.We address the second problem proposing a variant of the well-known Generalized Hough Transform (GHT), which is a robust object identification technique for unsegmented images. Moreover, we solve the first problem modifying the GHT to deal with an inexact matching problem. In this paper, we show how this idea can be efficiently and accurately realized. Experimental results are shown with two different databases of real, unsegmented images.     

ALSBIR: A local-structure-based image retrieval

The general-purpose shape retrieval problem is a challenging task. Particularly, an ideal technique, which can work in clustered environment, meet the requirements of perceptual similarity measure on partial query and overcoming dimensionality curse and adverse environment, is in demand. This paper reports our study on one local structural approach that addresses these issues. Shape representation and indexing are two key points in shape retrieval. The proposed approach combines a novel local-structure-based shape representation and a new histogram indexing structure. The former makes possible partial shape matching of objects without the requirement of segmentation (separation) of objects from complex background, while the latter has an advantage on indexing performance. The search time is linearly proportional to the input complexity. In addition, the method is relatively robust under adverse environments. It is able to infer retrieval results from incomplete information of an input by first extracting consistent and structurally unique local neighborhood information from inputs or models, and then voting on the optimal matches. Thousands of images have been used to test the proposed concepts on sensitivity analysis, similarity-based retrieval, partial query and mixed object query. Very encouraging experimental results with respect to efficiency and effectiveness have been obtained.     

基于CSS图像的形状相似性检索

本文介绍了图像数据库中形状相似性检索的一种方法--曲率尺度空间（CSS）图像表示法,它结合一些全局参数能有效实现检索。本文使用圆形向量图的方法,解决了在匹 配过程中由于方向或起始点不同引起的循环位移,简化了匹配算法。在由1100张海洋生物图像和其它单个隔离物体图像组成的数据库上测试,结果表明了其有效性。     

A real-time matching system for large fingerprint databases

With the current rapid growth in multimedia technology, there is an imminent need for efficient techniques to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated in a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexing large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-ASIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the public domain     

Cluster-driven refinement for content-based digital image retrieval

Increasing application demands are pushing databases toward providing effective and efficient support for content-based retrieval over multimedia objects. In addition to adequate retrieval techniques, it is also important to enable some form of adaptation to users' specific needs. This paper introduces a new refinement method for retrieval based on the learning of the users' specific preferences. The proposed system indexes objects based on shape and groups them into a set of clusters, with each cluster represented by a prototype. Clustering constructs a taxonomy of objects by forming groups of closely-related objects. The proposed approach to learn the users' preferences is to refine corresponding clusters from objects provided by the users in the foreground, and to simultaneously adapt the database index in the background. Queries can be performed based solely on shape, or on a combination of shape with other features such as color. Our experimental results show that the system successfully adapts queries into databases with only a small amount of feedback from the users. The quality of the returned results is superior to that of a color-based query, and continues to improve with further use.     

Let''s talk about it: dialogues with multimedia databases: Database support for human activity

We describe two scenarios of user tasks in which access to multimedia data plays a significant role. Because current multimedia databases cannot support these tasks, we introduce three new requirements on multimedia databases: multimedia objects should be active objects, querying is an interaction process, and query processing uses multiple representations. We discuss three techniques to handle multimedia objects as active objects. Also, we introduce a promising database architecture to meet the new user requirements. Agents within the database handle objects' representations, and a search engine on top of a conventional database handles relevance feedback and multiple representations.     

Content-based image retrieval based on a fuzzy approach

A typical content-based image retrieval (CBIR) system would need to handle the vagueness in the user queries as well as the inherent uncertainty in image representation, similarity measure, and relevance feedback. We discuss how fuzzy set theory can be effectively used for this purpose and describe an image retrieval system called FIRST (fuzzy image retrieval system) which incorporates many of these ideas. FIRST can handle exemplar-based, graphical-sketch-based, as well as linguistic queries involving region labels, attributes, and spatial relations. FIRST uses fuzzy attributed relational graphs (FARGs) to represent images, where each node in the graph represents an image region and each edge represents a relation between two regions. The given query is converted to a FARG, and a low-complexity fuzzy graph matching algorithm is used to compare the query graph with the FARGs in the database. The use of an indexing scheme based on a leader clustering algorithm avoids an exhaustive search of the FARG database. We quantify the retrieval performance of the system in terms of several standard measures.