During the past two decades an enormous amount of visual information has been generated; as a result, content-based image retrieval (CBIR) has received considerable attention. In CBIR the image is... Show More

During the past two decades an enormous amount of visual information has been generated; as a result, content-based image retrieval (CBIR) has received considerable attention. In CBIR the image is used as a query to find the most similar images. One of the biggest challenges in CBIR system is to fill up the “semantic gap,” which is the gap between low-level visual features and the high-level semantic concepts of an image. In this paper, the authors have proposed a saliency-based CBIR system that utilizes the semantic information of image and users search intention. In the proposed model, firstly a significant region is identified with the help of method structured matrix decomposition (SMD) using high-level priors that highlight the prominent area of the image. After that, a two-dimensional principal component analysis (2DPCA) is used as a feature, which is compact and effectively used for fast recognition. Experiment results are validated on different image dataset having an extensive collection of semantic classifications.

Using satellite imagery and remote sensing data for supervised and self-supervised learning problems can be quite challenging when parts of the underlying datasets are missing due to natural... Show More

Using satellite imagery and remote sensing data for supervised and self-supervised learning problems can be quite challenging when parts of the underlying datasets are missing due to natural phenomena (clouds, fog, haze, mist, etc.). Solving this problem will improve remote sensing data augmentation and make use of it in a world where satellite imagery represents a great resource to exploit in any big data pipeline setup. In this paper, the authors present a generative adversarial network (GANs) model that can generate natural atmospheric noise that serves as a data augmentation preprocessing tool to produce input to supervised machine learning algorithms.

Information retrieval refers to a noteworthy system of identifying relevant information and recovering it through specific procedures from stored system. These technique is used in many... Show More

Information retrieval refers to a noteworthy system of identifying relevant information and recovering it through specific procedures from stored system. These technique is used in many differentiated applications that deal with subjective intelligence. Applications based on information retrieval are identified with various issues, for example, in technology domain, the sudden size changes of the objectives as they approach the sensor. If not taken care of appropriately, the altered changes can present substantial issues in information affiliation and position estimation. Under such a system, the meaning of the objective state is the fundamental advance for programmed comprehension of dynamic scenes. This is the reason of requirement of cognitive models for information retrieval. The existent models move around the connection between data list terms and records.

In this piece of work, the authors have attempted to classify four types of long duration arrhythmia electrocardiograms (ECG) using radial basis function network (RBFN). The data is taken from... Show More

In this piece of work, the authors have attempted to classify four types of long duration arrhythmia electrocardiograms (ECG) using radial basis function network (RBFN). The data is taken from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, and features are extracted using empirical mode decomposition (EMD) technique. For most informative contents average power (AP) and coefficient of dispersion (CD) are evaluated from six intrinsic mode function (IMFs) of EMD. Principal component analysis (PCA) is used for feature reduction for effective classification using RBFN. The performance is shown in the result section, and it is found that the classification accuracy is 95.98%.