Shyam K. Gupta

Region-based image retrieval using integrated color, shape, and location index

A technique to retrieve images by region matching using a combined feature index based on color, shape, and location is presented within the framework of MPEG-7. Dominant regions within each image are indexed using integrated color, shape, and location features. Various combinations of regions are also indexed. The resulting indices and related metadata are stored in a Hash structure, where similar images tend to form clusters. The retrieval process is non-cascading and images can be retrieved based on color, shape or location and also based on a combined color shape location index. Results obtained show that retrieval effectiveness increases in non-cascaded region-based querying by combined index.

K-means Clustering Algorithm for Categorical Attributes

Efficient partitioning of large data sets into homogeneous clusters is fundamental problem in data mining. The hierarchical clustering methods are not adaptable because of their high computational complexity. The K-means based algorithms give promising results for their efficiency. However their use in often limited to numeric data. The quality of clusters produced depends on the initialization of clusters and the order in which is based on the K-means philosophy but removes the numeric data limitation.

Dominant color region based indexing for CBIR

Our world is dominated by visual information and a tremendous amount of such information is being added day-by-day. It would be impossible to cope with this explosion of visual data, unless they are organized such that we can retrieve them efficiently and effectively. The main problem in organizing and managing such visual data is indexing, the assignment of a synthetic descriptor which facilitates its retrieval. It involves extracting relevant entities or characteristics from images as index keys. Then a representation is chosen for the keys and specific meaning is assigned to it. Color is an important cue for content based image retrieval (CBIR) systems. We propose a technique to index and store images based on dominant color regions. Features like region size and location of the region are extracted and used as similarity measures. Images with similar indices are stored as an image cluster in a Hash table. A prototype of the retrieval system is developed using the JAVA language.

Color and Shape Index for Region-Based Image Retrieval

Most CBIR systems use low-level visual features for representation and retrieval of images. Generally such methods suffer from the problems of high-dimensionality leading to more computational time and inefficient indexing and retrieval performance. This paper focuses on a low-dimensional color and shape based indexing technique for achieving efficient and effective retrieval performance. We propose a combined index using color and shape features. A new shape similarity measure is proposed which is shown to be more effective. Images are indexed by dominant color regions and similar images form an image cluster stored in a hash structure. Each region within an image is further indexed by a region-based shape index. The shape index is invariant to translation, rotation and scaling. A JAVA based query engine supporting query-by-example is built to retrieve images by color and shape. The retrieval performance is studied and compared with a region-based shape indexing scheme.

A data-mining approach for optimizing performance of an incremental crawler

Crawlers visit the Web to maintain a local repository of Web pages up to date. We introduce another perspective to build an effective incremental crawler. Based on previous work in this field, we study how we can improve the performance of a crawler using data-mining. The information collected from the users can help the crawler to know which are the popular pages and to revisit them as soon as possible.

Architecture for knowledge discovery and knowledge management

In this paper, we propose I-MIN model for knowledge discovery and knowledge management in evolving databases. The model splits the KDD process into three phases. The schema designed during the first phase, abstracts the generic mining requirements of the KDD process and provides a mapping between the generic KDD process and (user) specific KDD subprocesses. The generic process is executed periodically during the second phase and windows of condensed knowledge called knowledge concentrates are created. During the third phase, which corresponds to actual mining by the end users, specific KDD subprocesses are invoked to mine knowledge concentrates. The model provides a set of mining operators for the development of mining applications to discover and renew, preserve and reuse, and share knowledge for effective knowledge management. These operators can be invoked by either using a declarative query language or by writing applications.The architectural proposal emulates a DBMS like environment for the managers, administrators and end users in the organization. Knowledge management functions, like sharing and reuse of the discovered knowledge among the users and periodic updating of the discovered knowledge are supported. Complete documentation and control of all the KDD endeavors in an organization are facilitated by the I-MIN model. This helps in structuring and streamlining the KDD operations in an organization.

Utilizing Network Features for Privacy Violation Detection

Privacy, its violations and techniques to circumvent privacy violation have grabbed the centre-stage of both academia and industry in recent months. Corporations worldwide have become conscious of the implications of privacy violation and its impact on them and to other stakeholders. Moreover, nations across the world are coming out with privacy protecting legislations to prevent data privacy violations. Such legislations however expose organizations to the issues of intentional or unintentional violation of privacy data. A violation by either malicious external hackers or by internal employees can expose the organizations to costly litigations. In this paper, we propose PRIVDAM; a data mining based intelligent architecture of a privacy violation detection and monitoring system whose purpose is to detect possible privacy violations and to prevent them in the future. This paper elaborates on the use of network characteristics for differentiating between normal network traffic and potential malicious attacks. These attacks are usually hidden in common network services like http, ftp, udp etc. Experimental evaluations illustrate that our approach is scalable as well as robust and accurate in detecting privacy violations

An integrated approach for range image segmentation and representation

Abstract This paper proposes an efficient method for the segmentation and representation of 3D rigid, solid objects from its range images using differential invariants derived from classical differential geometry. An efficient algorithm for derivation of surface curvatures, which are affine invariants, at smooth surface patches is proposed. The surface is approximated by Bezier and Beta-splines to compare qualitatively the proposed segmentation scheme. This scheme leads to derivation of surface features, which provides a very robust surface segmentation. An integrated approach represents the surface in terms of plane, quadric and superquadric surface. Experiments show excellent performance and together with the inherent parallelism make the scheme a promising one. Present experiments were conducted on some real range images where most of the parts of the object are planar.

Malafide intension based detection of privacy violation in information system

In the past few years there has been an increased focus on privacy issues for Information Systems. This has resulted in concerted systematic work focused on regulations, tools and enforcement. Despite this, privacy violations still do take place. Therefore there is an increased need to develop efficient methods to detect privacy violations. After a privacy violation has taken place, the post-event diagnostics should make use of any post-event information which might be available. This information (malafide intention) might play a decisive role in determining violations. In this paper we propose one such framework which makes use of malafide intentions. The framework is based on the hypothesis that any intrusion/unauthorized access has a malafide intention always associated with it and is available in a post-event scenario. We hereby propose that by analyzing the privacy policies and the available malafide intention, it is possible to detect probable privacy violations.

Query rewriting for detection of privacy violation through inferencing

When a privacy violation is detected the intension behind the violation is revealed. We refer to this as a malafide intension and the information revealed as the target. The target can be expressed using an SQL-like syntax. In sophisticated privacy attacks the target of the attack may not have been directly accessed but inferred from other pieces of information by exploiting functional dependencies present in the application domain. In this paper we present an efficient algorithm to rewrite the malafide intension query attributes which will return the minimal set of attribute from which the target can be derived. The attribute sets returned by algorithm can derive the target using functional dependencies (algorithm is sound) and furthermore if any minimal set can derive the target using functional dependencies then it will be returned by the algorithm (algorithm is complete).