Segment Oriented Compression Scheme for MOLAP Based on Extendible Multidimensional Arrays

Many statistical and MOLAP applications use multidimensional arrays as the basic data structure to allow the efficient and convenient storage and retrieval of large volumes of business data for decision making. Allocation of data or data compression is a key performance factor for this purpose because performance strongly depends on the amount of storage required and availability of memory. This holds especially for data warehousing environments in which huge amounts of data have to be dealt with. The most evident consequence of data compression is that it reduces storage cost by packing more logical data per unit of physical capacity. And improved performance is a net outcome because less physical data need to be retrieved during scan-oriented queries. In this paper, an efficient data compression technique is proposed based on the notion of extendible array. The main idea of the scheme is to compress each of the segments of the extendible array using the position information only. We compare the proposed scheme for different performance issues with prominent compression schemes.</p

Human action representation and recognition: An approach to histogram od spatiotemporal templates

The motion sequences of human actions have its own discriminating profile that can be represented as a spatiotemporal template like Motion History Image (MHI). A histogram is a popular statistic to present the underlying information in a template. In this paper a histogram oriented action recognition method is presented. In the proposed method, we use the Directional Motion History Images (DMHI), their corresponding Local Binary Pattern (LBP) images and the Motion Energy Image (MEI) as spatiotemporal template. The intensity histogram is then extracted from those images which are concatenated together to form the feature vector for action representation. A linear combination of the histograms taken from DMHIs and LBP images is used in the experiment. We evaluated the performance of the proposed method along with some variants of it using the renowned KTH action dataset and found higher accuracies. The obtained results justify the superiority of the proposed method compared to other approaches for action recognition found in literature

SpatioTemporal LBP and shape feature for human activity representation and recognition

In this paper, we propose a histogram based feature to represent and recognize human action in video sequences. Motion History Image (MHI) merges a video sequence into a single image. However, in this method, we use Directional Motion History Image (DMHI) to create four directional spatiotemporal templates. We, then, extract the Local Binary Pattern (LBP) from those templates. Then, spatiotemporal LBP histograms are formed to represent the distribution of those patterns which makes the feature vector. We also use shape feature taken from three selective snippets and concatenate them with the LBP histograms. We measure the performance of the proposed representation method along with some variants of it by experimenting on the Weizmann action dataset. Higher recognition rates found in the experiment suggest that, compared to complex representation, the proposed simple and compact representation can achieve robust recognition of human activity for practical use

Saliency Detection using Boundary Aware Regional Contrast Based Seam-map

Most of the saliency detection methods use the contrast and boundary prior to extract the salient region of an input image. These two approaches are followed in Boundary Aware Regional Contrast Based Visual Saliency Detection (BARC) [1] along with spatial distance information to achieve state of the art result. In this research, a more interesting cue is introduced to extract the salient region from an input image. Here, a combination of seam map and BARC [1] is presented to produce the saliency output. Seam importance map with boundary prior is also presented to measure the performance of this combination. Experiments with ten state of the art methods reveal that we get better saliency output by combining seam information of an input image with BARC [1] .International Conference on Innovation in Engineering and Technology(ICIET) 2018, 27 - 28 December, 2018, Dhaka, Banglades