Saurav Ghosh

Bio: Saurav Ghosh is an academic researcher from University of Calcutta. The author has contributed to research in topics: Feature extraction & Contextual image classification. The author has an hindex of 10, co-authored 34 publications receiving 284 citations. Previous affiliations of Saurav Ghosh include Information Technology University.

Performance comparison of feature vector extraction techniques in RGB color space using block truncation coding for content based image classification with discrete classifiers

Abstract: Content based image classification is a vital component of machine learning and is attaining increasing importance in the field of image processing. This paper has carried out widespread comparison of block truncation coding based techniques for feature vector extraction of images which is a precursor of image classification. A new block truncation coding (BTC) based technique using even and odd image parts for feature vector extraction is also introduced to perform image classification. The performances of classifier algorithms are compared in Receiver Operating Characteristic (ROC) Space. Two different categories of classifiers viz. K Nearest Neighbor (KNN) Classifier and RIDOR Classifier are being used to observe the degree of classification for various techniques under six different feature vector extraction environments.

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

Abstract: 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.

Multilevel Block Truncation Coding with diverse color spaces for image classification

Abstract: The paper depicts the use of Multilevel Block Truncation Coding for image classification. Feature vectors are extracted with four levels of Block Truncation Coding to classify the several categories of images for performance comparison in six different color spaces for the proposed methodology. Three databases out of which two are public databases and one is a generic database are considered for the experimentation. The two public datasets used are Coil Dataset and the Ponce Group 3D Photography Dataset respectively. The performance of the proposed classifier is tested on all three databases considered. In each of the considered color spaces improved performance is being observed with increasing levels of BTC and BTC level 4 is proved to be better as compared to other BTC levels. Overall Kekre's LUV color space has shown the best performance for BTC level 4 based image classification.

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

Abstract: 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

Image Classification using Block Truncation Coding with Assorted Color Spaces

Abstract: 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.

Color Appearance Models

Abstract: Color science is a multidisciplinary field with broad applications in industries such as digital imaging, coatings and textiles, food, lighting, archiving, art, and fashion. Accurate definition and measurement of color appearance is a challenging task that directly affects color reproduction in such applications. Color Appearance Models addresses those challenges and offers insight into the preferred solutions. Extensive research on the human visual system (HVS) and color vision has been performed in the last century, and this book contains a good overview of the most important and relevant literature regarding color appearance models.

Content-Based Image Retrieval Using Color, Shape and Texture Descriptors and Features

Abstract: Due to the recent technology development, the multimedia complexity is noticeably increased and new research areas are opened relying on similar multimedia content retrieval. Content-based image retrieval (CBIR) systems are used for the retrieval of images related to the Query Image (QI) from huge databases. The CBIR systems available today have confined efficiency as they extract only limited feature sets. This paper demonstrates the extraction of vast robust and important features from the images database and the storage of these features in the repository in the form of feature vectors. The feature repository contains color signature, the shape features and texture features. Here, features are extracted from specific QI. Accordingly, an innovative similarity evaluation with a metaheuristic algorithm (genetic algorithm with simulating annealing) has been attained between the QI features and those belonging to the database images. For an image entered as QI from a database, the distance metrics are used to search the related images, which is the main idea of CBIR. The proposed CBIR techniques are described and constructed based on RGB color with neutrosophic clustering algorithm and Canny edge method to extract shape features, YCbCr color with discrete wavelet transform and Canny edge histogram to extract color features, and gray-level co-occurrence matrix to extract texture features. The combination of these methods increases the image retrieval framework performance for content-based retrieval. Furthermore, the results’ precision–recall value is calculated to evaluate the system’s efficiency. The CBIR system proposed demonstrates better precision and recall values compared to other state-of-the-art CBIR systems.

A Novel Image Retrieval Based on Visual Words Integration of SIFT and SURF.

Abstract: With the recent evolution of technology, the number of image archives has increased exponentially. In Content-Based Image Retrieval (CBIR), high-level visual information is represented in the form of low-level features. The semantic gap between the low-level features and the high-level image concepts is an open research problem. In this paper, we present a novel visual words integration of Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF). The two local features representations are selected for image retrieval because SIFT is more robust to the change in scale and rotation, while SURF is robust to changes in illumination. The visual words integration of SIFT and SURF adds the robustness of both features to image retrieval. The qualitative and quantitative comparisons conducted on Corel-1000, Corel-1500, Corel-2000, Oliva and Torralba and Ground Truth image benchmarks demonstrate the effectiveness of the proposed visual words integration.

Image Fusion Techniques: A Survey

Abstract: The necessity of image fusion is growing in recently in image processing applications due to the tremendous amount of acquisition systems. Fusion of images is defined as an alignment of noteworthy Information from diverse sensors using various mathematical models to generate a single compound image. The fusion of images is used for integrating the complementary multi-temporal, multi-view and multi-sensor Information into a single image with improved image quality and by keeping the integrity of important features. It is considered as a vital pre-processing phase for several applications such as robot vision, aerial, satellite imaging, medical imaging, and a robot or vehicle guidance. In this paper, various state-of-art image fusion methods of diverse levels with their pros and cons, various spatial and transform based method with quality metrics and their applications in different domains have been discussed. Finally, this review has concluded various future directions for different applications of image fusion.