Rajen B. Bhatt

FRCT: fuzzy-rough classification trees

Using fuzzy-rough hybrids, we have proposed a measure to quantify the functional dependency of decision attribute(s) on condition attribute(s) within fuzzy data. We have shown that the proposed measure of dependency degree is a generalization of the measure proposed by Pawlak for crisp data. In this paper, this new measure of dependency degree has been encapsulated into the decision tree generation mechanism to produce fuzzy-rough classification trees (FRCT); efficient, top-down, multi-class decision tree structures geared to solving classification problems from feature-based learning examples. The developed FRCT generation algorithm has been applied to 16 real-world benchmark datasets. It is experimentally compared with the five fuzzy decision tree generation algorithms reported so far, and the rough decomposition tree algorithm. Comparison has been made in terms of number of rules, average training time, and classification accuracy. Experimental results show that the proposed algorithm to generate FRCT outperforms existing fuzzy decision tree generation techniques and rough decomposition tree induction algorithm.

Efficient Skin Region Segmentation Using Low Complexity Fuzzy Decision Tree Model

We propose an efficient skin region segmentation methodology using low complexity fuzzy decision tree constructed over B, G, R colour space. Skin and nonskin training dataset has been generated by using various skin textures obtained from face images of diversity of age, gender, and race people and nonskin pixels obtained from arbitrary thousands of random sampling of nonskin textures. Compact fuzzy model with very few numbers of rules allow to raster scan consumer photographs and classify each pixel as skin or nonskin for various face and human detection applications for embedded platforms.

Adaptive Digital Makeup

A gender and skin color ethnicity based automatic digital makeup system is presented. An automatic face makeup system which applies example based digital makeup based on skin ethnicity color and gender type. One major advantage of the system is that the makeup is based on the skin color and gender type, which is very necessary for an effective makeup. Another strong advantage is that it applies automatic makeup without requiring any user input.

Efficient general genre video abstraction scheme for embedded devices using pure audio cues

In this paper, we propose a framework of general genre (e.g., action, comedy, drama, documentary, musical, etc…) movie video abstraction scheme for embedded devices based on pure audio. The proposed algorithm does chaptering of multi-genre movie videos by detecting silence, environmental noise, pure speech, music (pure instrumental music and music with vocals, i.e., songs), and speech with back ground music (or music without vocals but with speech). Various audio features along with supervised classification strategies have been used for the abstraction. The current system has been evaluated with Gaussian Mixture Model (GMM) and Fuzzy Decision Tree (FDT) classifiers. The silence and environmental noise have been detected using the threshold approach with certain combination of audio features. Various optimizations done at algorithm and program level have made the scheme highly suitable for embedded devices.

Neuro-fuzzy decision trees for content popularity model and multi-genre movie recommendation system over social network

In this paper, we propose a framework of multi-genre movie recommender system based on neuro-fuzzy decision tree (NFDT) methodology. The system is capable of recommending list of movies in descending order of preference in response to user queries and profiles. The system also takes care of attempt to vote stuffing using novel application of fuzzy c-means clustering algorithm. Typical user query and profiles consists of content ratings for multiple genres like Action, Comedy, Drama, Music and many others. The distinctive point of the proposed approach is to handle recommender system generation as a supervised pattern classification problem, where in user reviews for multiple genres are conditions and overall star ratings are decisions. The entire recommender system is represented in the form of NFDT. Rules represented by NFDT also acts as a tool for understanding the combinations of contents driving popularity (and unpopularity) over certain social network. We have also proposed a modified inference mechanism based on matching and ordering of firing strength of each fuzzy decision tree path in response to user queries. The computational experiments have been presented on a sample real-world movie review database to judge the efficiency of the proposed recommender system.

Categorization and Reorientation of Images Based on Low Level Features

A hierarchical system to perform automatic categorization and reorientation of images using content analysis is pre-sented. The proposed system first categorizes images to some a priori defined categories using rotation invariant features. At the second stage, it detects their correct orientation out of {0o, 90o, 180o, and 270o} using category specific model. The system has been specially designed for embedded devices applications using only low level color and edge features. Machine learning algorithms optimized to suit the embedded implementation like support vector machines (SVMs) and scalable boosting have been used to develop classifiers for categorization and orientation detection. Results are presented on a collection of about 7000 consumer images collected from open resources. The proposed system finds it applications to various digital media products and brings pattern recognition solutions to the consumer electronics domain.

Hierarchical System for Content Based Categorization and Orientation of Consumer Images

A hierarchical framework to perform automatic categorization and reorientation of consumer images based on their content is presented. Sometimes the consumer rotates the camera while taking the photographs but the user has to later correct the orientation manually. The present system works in such cases; it first categorizes consumer images in a rotation invariant fashion and then detects their correct orientation. It is designed to be fast, using only low level color and edge features. A recently proposed information theoretic feature selection method is used to find most discriminant subset of features and also to reduce the dimension of feature space. Learning methods are used to categorize and detect the correct orientation of consumer images. Results are presented on a collection of about 7000 consumer images, collected by an independent testing team, from the internet and personal image collections.

Image Segmentation by Histogram Adaptive Fuzzification

We propose a simple image segmentation methodology using adaptive fuzzification over gray level histogram. The threshold has been estimated at the first intersection of back ground and fore ground fuzzy membership functions. The proposed methodology has been tested over various well known image segmentation problems.