Shamik Sural

Segmentation and histogram generation using the HSV color space for image retrieval

We have analyzed the properties of the HSV (hue, saturation and value) color space with emphasis on the visual perception of the variation in hue, saturation and intensity values of an image pixel. We extract pixel features by either choosing the hue or the intensity as the dominant property based on the saturation value of a pixel. The feature extraction method has been applied for both image segmentation as well as histogram generation applications - two distinct approaches to content based image retrieval (CBIR). Segmentation using this method shows better identification of objects in an image. The histogram retains a uniform color transition that enables us to do a window-based smoothing during retrieval. The results have been compared with those generated using the RGB color space.

Credit Card Fraud Detection Using Hidden Markov Model

Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a hidden Markov model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.

Similarity between Euclidean and cosine angle distance for nearest neighbor queries

Understanding the relationship among different distance measures is helpful in choosing a proper one for a particular application. In this paper, we compare two commonly used distance measures in vector models, namely, Euclidean distance (EUD) and cosine angle distance (CAD), for nearest neighbor (NN) queries in high dimensional data spaces. Using theoretical analysis and experimental results, we show that the retrieval results based on EUD are similar to those based on CAD when dimension is high. We have applied CAD for content based image retrieval (CBIR). Retrieval results show that CAD works no worse than EUD, which is a commonly used distance measure for CBIR, while providing other advantages, such as naturally normalized distance.

Credit card fraud detection: A fusion approach using Dempster-Shafer theory and Bayesian learning

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster-Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the extent of its deviation from good pattern. Dempster-Shafers theory is used to combine multiple such evidences and an initial belief is computed. The transaction is classified as normal, abnormal or suspicious depending on this initial belief. Once a transaction is found to be suspicious, belief is further strengthened or weakened according to its similarity with fraudulent or genuine transaction history using Bayesian learning. Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.

Automatic detection of human fall in video

In this paper, we present an approach for human fall detection, which has important applications in the field of safety and security. The proposed approach consists of two parts: object detection and the use of a fall model. We use an adaptive background subtraction method to detect a moving object and mark it with its minimum-bounding box. The fall model uses a set of extracted features to analyze, detect and confirm a fall. We implement a two-state finite state machine (FSM) to continuously monitor people and their activities. Experimental results show that our method can detect most of the possible types of single human falls quite accurately.

An Integrated Color and Intensity Co-occurrence Matrix

The paper presents a novel approach for representing color and intensity of pixel neighborhoods in an image using a co-occurrence matrix. After analyzing the properties of the HSV color space, suitable weight functions have been suggested for estimating relative contribution of color and gray levels of an image pixel. The suggested weight values for a pixel and its neighbor are used to construct an Integrated Color and Intensity Co-occurrence Matrix (ICICM). We have shown that if the ICICM matrix is used as a feature in an image retrieval application, it is possible to have higher recall and precision compared to other existing methods.

STARBAC: spatiotemporal role based access control

Role Based Access Control (RBAC) has emerged as an important access control paradigm in computer security. However, the access decisions that can be taken in a system implementing RBAC do not include many relevant factors like user location, system location, system time, etc. We propose a spatiotemporal RBAC Model (STARBAC) which reasons in spatial and temporal domain in tandem. STARBAC control command enables or disables role based on spatiotemporal conditions. The new model is able to specify a number of different types of important access requirements not expressible in existing variations of RBAC model like GEORBAC and TRBAC. The specification language we present here is powerful enough to allow logical connectives like AND (∧) and OR (∨) over spatiotemporal conditions.

ANN- and PSO-Based Synthesis of On-Chip Spiral Inductors for RF ICs

This paper presents an efficient layout-level synthesis approach for RF planar on-chip spiral inductors. A spiral inductor is modeled using artificial neural networks in which the layout design parameters, namely, spiral outer diameter, number of turns, width of metal traces, and metal spacing, are taken as input. Inductance, quality factor (Q), and self-resonance frequency (SRF) form the output of the neural model. Particle-swarm optimization is used to explore the layout space to achieve a given target inductance meeting the SRF and other constraints. Our synthesis approach provides multiple sets of layout parameters that help a designer in the tradeoff analysis between conflicting objectives, such as area, Q, and SRF for a target-inductance value. We present several synthesis results which show good accuracy with respect to full-wave electromagnetic (EM) simulations. Since the proposed procedure does not require an EM simulation in the synthesis loop, it substantially reduces the cycle time in RF-circuit design optimization.

Database Intrusion Detection using Weighted Sequence Mining

Data mining is widely used to identify interesting, potentially useful and understandable patterns from a large data repository. With many organizations focusing on web-based on-line transactions, the threat of security violations has also increased. Since a database stores valuable information of an application, its security has started getting attention. An intrusion detection system (IDS) is used to detect potential violations in database security. In every database, some of the attributes are considered more sensitive to malicious modifications compared to others. We propose an algorithm for finding dependencies among important data items in a relational database management system. Any transaction that does not follow these dependency rules are identified as malicious. We show that this algorithm can detect modification of sensitive attributes quite accurately. We also suggest an extension to the Entity- Relationship (E-R) model to syntactically capture the sensitivity levels of the attributes.

Gait recognition using Pose Kinematics and Pose Energy Image

Many of the existing gait recognition approaches represent a gait cycle using a single 2D image called Gait Energy Image (GEI) or its variants. Since these methods suffer from lack of dynamic information, we model a gait cycle using a chain of key poses and extract a novel feature called Pose Energy Image (PEI). PEI is the average image of all the silhouettes in a key pose state of a gait cycle. By increasing the resolution of gait representation, more detailed dynamic information can be captured. However, processing speed and space requirement are higher for PEI than the conventional GEI methods. To overcome this shortcoming, another novel feature named as Pose Kinematics is introduced, which represents the percentage of time spent in each key pose state over a gait cycle. Although the Pose Kinematics based method is fast, its accuracy is not very high. A hierarchical method for combining these two features is, therefore, proposed. At first, Pose Kinematics is applied to select a set of most probable classes. Then, PEI is used on these selected classes to get the final classification. Experimental results on CMUs Mobo and USFs HumanID data set show that the proposed approach outperforms existing approaches.