Saurav Ghosh

Performance Boost of Block Truncation Coding based Image Classification using Bit Plane Slicing

Image classification demands major attention with increasing volume of available image data. The paper has shown performance boosting of image classification after associating Bit Plane Slicing with Block Truncation Coding (BTC) for feature extraction. Here more significant bit planes were considered for extraction of feature vectors. RGB color space was considered to carry out the experimentation. A database of 900 images was used for evaluation purpose. KeywordsPlane Slicing, BTC, CBIC, RGB

A Novel Feature Extraction Technique Using Binarization of Bit Planes for Content Based Image Classification

A number of techniques have been proposed earlier for feature extraction using image binarization. Efficiency of the techniques was dependent on proper threshold selection for the binarization method. In this paper, a new feature extraction technique using image binarization has been proposed. The technique has binarized the significant bit planes of an image by selecting local thresholds. The proposed algorithm has been tested on a public dataset and has been compared with existing widely used techniques using binarization for extraction of features. It has been inferred that the proposed method has outclassed all the existing techniques and has shown consistent classification performance.

Image Classification Using Advanced Block Truncation Coding with Ternary Image Maps

Incredible escalation of Information Technology leads to generation, storage and transfer of enormous information. Easy and round the clock access of data has been made possible by virtue of world wide web. The high capacity storage devices and communication links facilitates the archiving of information in the form of multimedia. This type of information comprises of images in majority and is growing in number by leaps and bounds. But the usefulness of this information will be at stake if maximum information is not retrieved in minimum time. The huge database of information comprising of multiple number of image data is diversified mix in nature. Proper Classification of Image data based on their content is highly applicable in these databases to form limited number of major categories. The novel ternary block truncation coding (Ternary BTC) is proposed in the paper, also the comparison of Binary block truncation coding (Binary BTC) and Ternary Block Truncation Coding is done for image classification. Here two image databases are considered for experimentation. The proposed ternary BTC is found to be better than Binary BTC for image classification as indicated by higher average success rate.

Image Classification using Block Truncation Coding with Assorted Color Spaces

The paper portrays comprehensive performance comparison of image classification techniques using block truncation coding (BTC) with assorted color spaces. Overall six color spaces have been explored which includes RGB color space for applying BTC to figure out the feature vector in Content Based Image Classification (CBIC) techniques. A generic database with 900 images having 100 images per category spread across 9 different categories have been considered to conduct the experimentation with the proposed Image Classification technique. On the whole nine hundred queries have been fired. The average success rate of class determination for each of the color spaces has been computed and considered for performance analysis. The results explicitly reveal performance improvement (higher average success rate values) with proposed colorBTC methods with luminance chromaticity color spaces compared to RGB color space. Best result is shown by YUV color space based BTC in content based image classification.

Feature Extraction with Ordered Mean Values for Content Based Image Classification

Categorization of images into meaningful classes by efficient extraction of feature vectors from image datasets has been dependent on feature selection techniques. Traditionally, feature vector extraction has been carried out using different methods of image binarization done with selection of global, local, or mean threshold. This paper has proposed a novel technique for feature extraction based on ordered mean values. The proposed technique was combined with feature extraction using discrete sine transform (DST) for better classification results using multitechnique fusion. The novel methodology was compared to the traditional techniques used for feature extraction for content based image classification. Three benchmark datasets, namely, Wang dataset, Oliva and Torralba (OT-Scene) dataset, and Caltech dataset, were used for evaluation purpose. Performance measure after evaluation has evidently revealed the superiority of the proposed fusion technique with ordered mean values and discrete sine transform over the popular approaches of single view feature extraction methodologies for classification.

A dominating set based modified LEACH using Ant Colony Optimization for data gathering in WSN

Data gathering from unreachable terrains such as volcanic area, dense forest, deep sea floor and others are a major application area of Wireless Sensor Networks (WSN). The battery operated sensor nodes are of limited energy and it is necessary to preserve their energy to elongate the lifetime of WSN. Hierarchical routing protocols (HRP) like LEACH, PEGASIS disseminate data to the Base Station (BS) by assigning energy intensive data communication to high residual energy nodes while others are engaged in local communication with an overall objective of load balanced and energy efficient data routing. A major drawback of HRP is data redundancy as all nodes gathers data in a round where an area is sensed by multiple nodes. A dominating set formation (DSF) is formulated for constructing a dominating set (DS) from the deployed nodes. We propose a proactive HRP LEACH-DS-ACO by modifying the basic LEACH. DSF is applied to each cluster to obtain its DS from which a cluster chain using ACO is constructed and a chain leader is elected depending on its residual energy and proximity to BS. LEACH-DS-ACO is simulated on MATLAB platform and its performance is compared with LEACH, LEACH-C and PEGASIS. Simulation results indicate that LEACH-DS-ACO outperforms the rest in terms of network lifetime and is also load balanced. The results are shown to be statistically significant.

Novel Technique in Block Truncation Coding Based Feature Extraction for Content Based Image Identification

Feature vector extraction has been the key component to define the success rate for content based image recognition. Block truncation coding is a simple technique which has facilitated various methods for effective feature vector extraction for content based image recognition. A new technique named Sorted Block Truncation Coding (SBTC) has been introduced in this work. Three different public datasets namely Wang Dataset, Oliva and Torralba (OT-Scene) Dataset and Caltech Dataset consisting of 6,221 images on the whole was considered for evaluation purpose. The technique has stimulated superior performance in image recognition when compared to classification and retrieval results with other existing techniques of feature extraction. The technique was also evaluated in lossy compression domain for the test images. Various parameters like precision, recall, misclassification rate and F1 score has been considered to evaluate the performances. Statistical evaluations have been carried out for all the comparisons by introducing paired t test to establish the significance of the findings. Classification and retrieval with proposed technique has shown a minimum of 14.4 % rise in precision results compared to the existing state-of-the art techniques.

Enhanced PEGASIS using ant colony optimization for data gathering in WSN

Data gathering in an energy efficient and timely manner is one of the fundamental tasks of wireless sensor network (WSN). Applications where continual monitoring of inhabitable deployment area over a considerable time is required calls for proactive hierarchical data routing protocols. One of them i.e. PEGASIS routing protocol suffers from data redundancy and latency. This paper presents an enhanced version of PEGASIS (E-PEGASIS) which overcomes the drawbacks and is energy efficient. The simulation results indicate that E-PEGASIS extends the WSN lifetime in comparison to PEGASIS, Binary PEGASIS and LBEERA along with considerable reduction in data latency. The results are shown to be statistically significant.

ACOHC: Ant colony optimization based hierarchical clustering in wireless sensor network

Data gathering in an energy efficient and timely manner is the fundamental task of Wireless Sensor Network (WSN). The battery operated sensor nodes are of limited energy and it is necessary to preserve their battery power to elongate the lifetime of WSN. In Hierarchical Routing Protocol (HRP) some nodes transmit data to BS which is more energy intensive task while others are engaged in local communications which provides load balancing. In this paper we propose energy efficient and load balanced ant colony optimization based hierarchical data gathering method (ACOHC). The deployment area is divided into optimal KOPT number of clusters using K-means. The nodes in a cluster form a chain using ant colony optimization (ACO) with the election of a chain leader (CL). The CLs forms an upper level chain using ACO with the election of a super leader (SL) to transfer the final aggregated data to the BS. Simulation results indicate that ACOHC performs better in comparison to LEACH, LEACH-C, PEGASIS and KLEACH in terms of network lifetime, energy X delay product and throughput. The statistical significance of our results is established.

Decision fusion-based approach for content-based image classification

Purpose Current practices in data classification and retrieval have experienced a surge in the use of multimedia content. Identification of desired information from the huge image databases has been facing increased complexities for designing an efficient feature extraction process. Conventional approaches of image classification with text-based image annotation have faced assorted limitations due to erroneous interpretation of vocabulary and huge time consumption involved due to manual annotation. Content-based image recognition has emerged as an alternative to combat the aforesaid limitations. However, exploring rich feature content in an image with a single technique has lesser probability of extract meaningful signatures compared to multi-technique feature extraction. Therefore, the purpose of this paper is to explore the possibilities of enhanced content-based image recognition by fusion of classification decision obtained using diverse feature extraction techniques. Design/methodology/approach Three novel techniques of feature extraction have been introduced in this paper and have been tested with four different classifiers individually. The four classifiers used for performance testing were K nearest neighbor (KNN) classifier, RIDOR classifier, artificial neural network classifier and support vector machine classifier. Thereafter, classification decisions obtained using KNN classifier for different feature extraction techniques have been integrated by Z-score normalization and feature scaling to create fusion-based framework of image recognition. It has been followed by the introduction of a fusion-based retrieval model to validate the retrieval performance with classified query. Earlier works on content-based image identification have adopted fusion-based approach. However, to the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. Findings The proposed fusion techniques have successfully outclassed the state-of-the-art techniques in classification and retrieval performances. Four public data sets, namely, Wang data set, Oliva and Torralba (OT-scene) data set, Corel data set and Caltech data set comprising of 22,615 images on the whole are used for the evaluation purpose. Originality/value To the best of the authors’ knowledge, fusion-based query classification has been addressed for the first time as a precursor of retrieval in this work. The novel idea of exploring rich image features by fusion of multiple feature extraction techniques has also encouraged further research on dimensionality reduction of feature vectors for enhanced classification results.