Interactive relevance feedback mechanism for image retrieval using rough set

Recently, how to express human perception has become one of the most active research topics in image retrieval. In this paper, we propose a relevance feedback mechanism that can express objectively human perception by using rough set theory in retrieval system. The mechanism makes full use of the inherent advantages of rough set to solve the difficulty that the retrieval system cannot express human perception. In the end, an image retrieval system – “Basestar”, which is based on the technology, is introduced to illustrate the retrieval performance.     

Expert vision systems integrating image segmentation and recognition processes

In this paper, we propose a prototype rule-based system which integrates segmentation and recognition processes to analyze and classify objects in an image. This is quite different from the traditional image analysis paradigm which treats segmentation as a prerequisite for recognition and interpretation. There are four basic components in the system, i.e., low-level image processing, feature computation, domain-independent, and domain-dependent subsystems. In the low-level image processing subsystem, various “nonpurposive” operators are employed to divide the image into uniform and homogeneous regions based on the information of intensities. The feature computation subsystem extracts features of each individual region. The domain-independent subsystem employs weak knowledge to filter out “obviously impossible” regions while the domain-dependent subsystem uses domain-specific knowledge to improve the results and finally recognize the objects of interest in the image. Two sets of images are used to demonstrate the capability and flexibility of this system. One set consists of distributor caps (auto parts) of different shapes. The other set is composed of tomographical image pairs acquired by MRI and PET.     

A hybrid method for robust car plate character recognition

Image-based car plate recognition is an indispensable part of an intelligent traffic system. The quality of the images taken for car plates, especially for Chinese car plates, however, may sometimes be very poor, due to the operating conditions and distortion because of poor photographical environments. Furthermore, there exist some “similar” characters, such as “8” and “B”, “7” and “T” and so on. They are less distinguishable because of noises and/or distortions. To achieve robust and high recognition performance, in this paper, a two-stage hybrid recognition system combining statistical and structural recognition methods is proposed. Car plate images are skew corrected and normalized before recognition. In the first stage, four statistical sub-classifiers recognize the input character independently, and the recognition results are combined using the Bayes method. If the output of the first stage contains characters that belong to prescribed sets of similarity characters, structure recognition method is used to further classify these character images: they are preprocessed once more, structure features are obtained from them and these structure features are fed into a decision tree classifier. Finally, genetic algorithm is employed to achieve optimum system parameters. Experiments show that our recognition system is very efficient and robust. As part of an intelligent traffic system, the system has been in successful commercial use.     

Retrieval of movie scenes by semantic matrix and automatic feature weight update

A new semantic-based video scene retrieval method is proposed in this paper. Twelve low-level features extracted from a video clip are represented in a genetic chromosome and target videos that user has in mind are retrieved by the interactive genetic algorithm through the feedback iteration. In this procedure, high-level semantic relevance between retrieved videos is accumulated with so-called semantic relevance matrix and semantic frequency matrix for each iteration, and they are combined with an automatic feature weight update scheme to retrieve more target videos at the next iteration. Experiments over 300 movie scene clips extracted from latest well-known movies, showed an user satisfaction of 0.71 at the fourth iteration for eight queries such as “gloominess”, “happiness”, “quietness”, “action”, “conversation”, “explosion”, “war”, and “car chase”.     

Color enhancement of highly correlated images. I. Decorrelation and HSI contrast stretches

Conventional enhancements for the color display of multispectral images are based on independent contrast modifications or “stretches” of three input images. This approach is not effective if the image channels are highly correlated or if the image histograms are strongly bimodal or more complex. Any of several procedures that tend to “stretch” color saturation while leaving hue unchanged may better utilize the full range of colors for the display of image information. Two conceptually different enhancements are discussed: the “decorrelation stretch”, based on principal-component (PC) analysis, and the “stretch” of “hue”-“saturation”-intensity (HSI) transformed data. The PC transformation is scene-dependent, but the HSI transformation is invariant. Examples of images enhanced by conventional linear stretches, decorrelation stretch, and by stretches of HSI transformed data are compared. Schematic variation diagrams or two- and three-dimensional histograms are used to illustrate the “decorrelation stretch” method and the effect of the different enhancements.     

Unsupervised texture segmentation using Gabor filters

This paper presents a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain, and a systematic filter selection scheme is proposed, which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of “energy” in a window around each pixel. A square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial information in the clustering process is proposed. A relative index is used to estimate the “true” number of texture categories.     

Sketching space

In this paper, we present a sketch modelling system which we call Stilton. The program resembles a desktop VRML browser, allowing a user to navigate a three-dimensional model in a perspective projection, or panoramic photographs, which the program maps onto the scene as a “floor” and “walls”. We place an imaginary two-dimensional drawing plane in front of the user, and any geometric information that user sketches onto this plane may be reconstructed to form solid objects through an optimisation process. We show how the system can be used to reconstruct geometry from panoramic images, or to add new objects to an existing model. While panoramic imaging can greatly assist with some aspects of site familiarisation and qualitative assessment of a site, without the addition of some foreground geometry they offer only limited utility in a design context. Therefore, we suggest that the system may be of use in “just-in-time” CAD recovery of complex environments, such as shop floors, or construction sites, by recovering objects through sketched overlays, where other methods such as automatic line-retrieval may be impossible. The result of using the system in this manner is the “sketching of space” — sketching out a volume around the user — and once the geometry has been recovered, the designer is free to quickly sketch design ideas into the newly constructed context, or analyse the space around them. Although end-user trials have not, as yet, been undertaken we believe that this implementation may afford a user-interface that is both accessible and robust, and that the rapid growth of pen-computing devices will further stimulate activity in this area.     

Permitting updates through views of data bases

Providing different views (logical images of the structure of a data base) to various users creates the problem of determining how update operations expressed in terms of the views should affect the stored form of the data base. For data bases with a relational organization, we indicate the effects of a wide range of update operations on views. We conclude that some operations must be prohibited in order to assure harmonious interactions among data base users, but that many other operations can be allowed even though the structure of the view may differ substantially from the actual structure of the data base. We consider views not only as “windows” through which to see a data base in a particular way, but also as “shades” to conceal and protect information, and as “screens” to intercept any update operations that could leave the stored form of the data base in an unacceptable state.     

Applying CIM technology to image factories

Image processing allows the automation of the business transaction, turning “paper factories” into “image factories.” This burgeoning new area of computerization needs new paradigms, theories, and methods to maximize its effectiveness at cutting time and costs. This paper investigates what this new area of computerization can gain from CIM technology. It examines the similarities between the processing of computer images in a paperless business environment and the processing of materials into products in the manufacturing plant. These similarities will allow us to apply mature, well-tested CIM techniques to emerging image factories.     

General structure and characteristics of quick response production system

The diversification and shorter production cycles urge manufacturers to shift their production systems from “make to stock” to “make to order” or an intermediate production system between them. The present paper deals with a production system developed by some manufacturers to meet requirements in the last decade. The system, which we call “Quick response to orders production system” or in brief “quick response production system (QRPS)”, is characterized by two key factors, i.e., “acceptable response time” and “semifinished product.” An acceptable response time is decided through the explicit or implicit approval of customers beforehand and manufacturers guarantee to ship products within an acceptable response time in a stochastic sense by processing the semifinished product according to customer order. As a result, manufacturers are able to reduce the inventory of finished products, while customers are able to enjoy the diversification of products and a stable supply from manufacturers. The general structure and characteristics of QRPS are discussed primarily from a quantitative point of view and a numerical example is shown to assist in understanding the design and operation of QRPS.     

Graphical Search for Images by <Emphasis Type="Italic">PictureFinder</Emphasis>

The text searching paradigm still prevails even when users are looking for image data for example in the Internet. Searching for images mostly means searching on basis of annotations that have been made manually. When annotations are left empty, which is usually the case, searches on image file names are performed. This may lead to surprising retrieval results. The graphical search paradigm, searching image data by querying graphically, either with an image or with a sketch, currently seems not to be the preferred method partly because of the complexity in designing the query.In this paper we present our PictureFinder system, which currently supports “full image retrieval” in analogy to full text retrieval. PictureFinder allows graphical queries for the image the user has in his mind by sketching colored and/or textured regions or by whole images (query by example). By adjusting the search tolerances for each region and image feature (i.e. hue, saturation, lightness, texture pattern and coverage) the user can tune his query either to find images matching his sketch or images which differing from the specified colors and/or textures to a certain degree. To compare colors we propose a color distance measure that takes into account the fact that different colors spread differently in the color space, and which take into account that the position of a region in an image may be important.Furthermore, we show our query by example approach. Based on the example image chosen by the user, a graphical query is generated automatically and presented to the user. One major advantage of this approach is the possibility to change and adjust a query by example in the same way as a query which was sketched by the user. By deleting unimportant regions and by adjusting the tolerances of the remaining regions the user may focus on image details which are important to him.     

A distributed approach for fault detection and diagnosis based on Time Petri Nets

This paper proposes an algorithm for the model based design of a distributed protocol for fault detection and diagnosis for very large systems. The overall process is modeled as different Time Petri Net (TPN) models (each one modeling a local process) that interact with each other via guarded transitions that becomes enabled only when certain conditions (expressed as predicates over the marking of some places) are satisfied (the guard is true). In order to use this broad class of time DES models for fault detection and diagnosis we derive in this paper the timing analysis of the TPN models with guarded transitions. In this paper we also extend the modeling capability of the faults calling some transitions faulty when operations they represent take more or less time than a prescribed time interval corresponding to their normal execution. We consider here that different local agents receive local observation as well as messages from neighboring agents. Each agent estimates the state of the part of the overall process for which it has model and from which it observes events by reconciling observations with model based predictions. We design algorithms that use limited information exchange between agents and that can quickly decide “questions” about “whether and where a fault occurred?” and “whether or not some components of the local processes have operated correctly?”. The algorithms we derive allow each local agent to generate a preliminary diagnosis prior to any communication and we show that after communicating the agents we design recover the global diagnosis that a centralized agent would have derived. The algorithms are component oriented leading to efficiency in computation.     

A new approach for fuzzy information retrieval based on weighted power-mean averaging operators

An information retrieval system can help users to retrieve documents relevant to the users’ queries. In recent years, some researchers used averaging operators (i.e., Infinite–One operators, Waller–Kraft operators, P-Norm operators and GMA operators) to handle “AND” and “OR” operations of users’ fuzzy queries for fuzzy information retrieval, but they still have some drawbacks, e.g., sometimes query results do not coincide with the intuition of the human being. In this paper, we present new averaging operators, called weighted power-mean averaging (WPMA) operators, based on the weighted power mean for dealing with fuzzy information retrieval to overcome the drawbacks of the existing methods. Furthermore, we also extend the proposed WPMA operators into the extended WPMA operators to handle weighted fuzzy queries for fuzzy information retrieval. The proposed WPMA operators are more flexible and more intelligent than the existing averaging operators to handle users’ fuzzy queries for fuzzy information retrieval.     

Similarity-based ranking and query processing in multimedia databases

Since media-based evaluation yields similarity values, results to a multimedia database query, Q(Y₁,…,Y_n), is defined as an ordered list S_Q of n-tuples of the form X₁,…,X_n. The query Q itself is composed of a set of fuzzy and crisp predicates, constants, variables, and conjunction, disjunction, and negation operators. Since many multimedia applications require partial matches, S_Q includes results which do not satisfy all predicates. Due to the ranking and partial match requirements, traditional query processing techniques do not apply to multimedia databases. In this paper, we first focus on the problem of “given a multimedia query which consists of multiple fuzzy and crisp predicates, providing the user with a meaningful final ranking”. More specifically, we study the problem of merging similarity values in queries with multiple fuzzy predicates. We describe the essential multimedia retrieval semantics, compare these with the known approaches, and propose a semantics which captures the requirements of multimedia retrieval problem. We then build on these results in answering the related problem of “given a multimedia query which consists of multiple fuzzy and crisp predicates, finding an efficient way to process the query.” We develop an algorithm to efficiently process queries with unordered fuzzy predicates (sub-queries). Although this algorithm can work with different fuzzy semantics, it benefits from the statistical properties of the semantics proposed in this paper. We also present experimental results for evaluating the proposed algorithm in terms of quality of results and search space reduction.     

Filter-based models for pattern classification

In this paper we consider a technique for pattern classification based upon the development of prototypes which capture the distinguishing features (“disjunctive prototypes”) of each pattern class and, via cross-correlation with incoming test images, enable efficient pattern classification. We evaluate such a classification procedure with prototypes based on the images per se (direct code), Gabor scheme (multiple fixed filter representation) and an edge (scale space-based) coding scheme. Our analyses, and comparisons with human pattern classification performance, indicate that the edge-only disjunctive prototypes provide the most discriminating classification performance and are the more representative of human behaviour.     

Expert systems in the context of multi-brain systems

In this article, we postulate the possibility of building a four-brain like expert system. For this purpose we have taken ideas from Minsky’s “Society of Mind” some concepts from Brooks and Stein’s “Brains for Bodies” project, and some mechanisms from neurophysiology. In our search for conceptualization of a minimum multi-brain like system oriented to expert systems, we found that: (a) expert systems with blackboard architecture can be seen as primitive topological approaches to multi-brain systems, (b)in our multi-brain like expert systems conception, as in many other expert systems, we feel that we have to impose our external language as their internal language and our external symbols as their internal symbols. These are the kind of entities discussed by philosophers as “What is it like to be a Bat?”. In this context our conception is still a “Batman”. True-cognitive expert systems might be produced in the future if the matters are self constructed internal languages, and its post-hoc knowledge, and (c) in the scope of multi-brain systems, expert systems are not an engineering subject of artificial intelligence (AI) but a substantive subject that goes from philosophical to neurophysiological aspects.     

Biometric perils and patches

Biometrics authentication offers many advantages over conventional authentication systems that rely on possessions or special knowledge. With conventional technology, often the mere possession of an employee ID card is proof of ID, while a password potentially can be used by large groups of colleagues for long times without change. The fact that biometrics authentication is non-repudiable (hard to refute) and, yet, convenient, is among its most important advantages. Biometrics systems, however, suffer from some inherent biometrics-specific security threats. These threats are mainly related to the use of digital signals and the need for additional input devices, though we also discuss brute-force attacks of biometrics systems. There are also problems common to any pattern recognition system. These include “wolves” and “lambs”, and a new group we call “chameleons”. An additional issue with the use of biometrics is the invasion of privacy because the user has to enroll with an image of a body part. We discuss these issues and suggest some methods for mitigating their impact.     

A Multi-Resolution Content-Based Retrieval Approach for Geographic Images

Current retrieval methods in geographic image databases use only pixel-by-pixel spectral information. Texture is an important property of geographical images that can improve retrieval effectiveness and efficiency. In this paper, we present a content-based retrieval approach that utilizes the texture features of geographical images. Various texture features are extracted using wavelet transforms. Based on the texture features, we design a hierarchical approach to cluster geographical images for effective and efficient retrieval, measuring distances between feature vectors in the feature space. Using wavelet-based multi-resolution decomposition, two different sets of texture features are formulated for clustering. For each feature set, different distance measurement techniques are designed and experimented for clustering images in a database. The experimental results demonstrate that the retrieval efficiency and effectiveness improve when our clustering approach is used.