Multi-resolution Histograms Research Articles

Optimal transport based pyramid graph kernel for autism spectrum disorder diagnosis

Brain network, which characterizes the functional and structural interactions of brain regions with graph theory, has been widely utilized to diagnose brain diseases, such as autism spectrum disorder (ASD). It is a challenge to measure the network (or graph) similarity in brain network analysis. Graph kernel (i.e., kernel defined on graphs) offers an efficient tool for measuring the similarity of paired brain networks and yields the excellent classification performance in brain disease diagnosis. However, most of the existing graph kernels neglected the hierarchical architecture information of brain networks. To address this problem, in this paper, we propose an optimal transport based pyramid graph kernel for measuring brain network similarity and then apply it to brain disease classification. The main idea is to transform brain networks into pyramid structures, which reflect the hierarchical architecture information of the brain network with multi-resolution histograms. The optimal transport distance in pyramid structures is calculated for measuring transport costs between paired brain networks. Finally, the optimal transport based pyramid graph kernel is computed based on this optimal transport distance. To evaluate the effectiveness of the proposed optimal transport based pyramid graph kernel, the extensive experiments are performed in functional magnetic resonance imaging data of brain disease from the Autism Brain Imaging Data Exchange database. The experimental results show that our proposed optimal transport based pyramid graph kernel outperforms the state-of-the-art methods in ASD classification tasks.

Human Action Recognition Using Motion History Image Based Temporal Segmentation

A human action recognition system based on image depth is proposed in this paper. Depth information features are not easily disturbed by noise; and due to this characteristic, the system can quickly extract foreground targets. Moreover, the target data, namely, depth and two-dimensional (2D) data, are projected to three orthogonal planes. In this manner, the action featured in the depth motion along the optical axis can clearly describe the trajectory. Based on the change of motion energy and the angle variations of motion orientations, the temporal segmentation (TS) method automatically segments the complex action into several simple movements. Three-dimensional (3D) data is further applied to acquire the three-viewpoint (3V) motion history trajectory, whereby a target’s motion is described through the motion history images (MHIs) from the 3Vs. The weightings corresponding to the gradients of the MHIs are included for determining the viewpoint that bests describe the target’s motion. In terms of feature extraction, the application of multi-resolution motion history histograms can effectively reduce the computational load and achieve a high recognition rate. Experimental results demonstrate that the proposed method can effectively solve the self-occlusion problem.

Progressive Filtering Approach for Query by Humming System Through Empirical Mode Decomposition and Multiresolution Histograms

AbstractThis research work proposes an implementation of adept content-based music retrieval technique that attempts to address the demands of the rising availability of digital music. The primary objective of this research work is to balance the perilous impact of non-relevant songs through progressive filtering (PF) for query by humming (QBH) music information retrieval system. The PF is a technique of searching in manifolds for problem solving through reduced search space. A new strategy for empirical mode decomposition (EMD) analysis is adopted, and outcomes are propelled as a significant source of information for multiresolution histogram (MRH) representations. Finally, the concept of PF is realized through MRH to retrieve the desired song by humming. The proposed multiresolution QBH system is evaluated on a music database of 1200 fragments, and the experimental results effectively locate the humming patterns and recite the eminence of EMD analysis for retrieving the desired song through humming.

Digital Image Forensics Using Multi-Resolution Histograms

In this paper, the authors investigate the prospect of using multi-resolution histograms (MRH) in conjunction with digital image forensics, particularly in the detection of two kinds of copy-move manipulations, i.e., cloning and splicing. To the best of the authors’ knowledge, this is the first work that uses the same feature in both cloning and splicing forensics. The experimental results show the simplicity and efficiency of using MRH for the purpose of clone detection and splicing detection.

Morphological Description of Color Images for Content-Based Image Retrieval

Placed within the context of content-based image retrieval, we study in this paper the potential of morphological operators as far as color description is concerned, a booming field to which the morphological framework, however, has only recently started to be applied. More precisely, we present three morphology-based approaches, one making use of granulometries independently computed for each subquantized color and two employing the principle of multiresolution histograms for describing color, using respectively morphological levelings and watersheds. These new morphological color descriptors are subsequently compared against known alternatives in a series of experiments, the results of which assert the practical interest of the proposed methods.

Multiresolution histograms and their use for recognition

The histogram of image intensities is used extensively for recognition and for retrieval of images and video from visual databases. A single image histogram, however, suffers from the inability to encode spatial image variation. An obvious way to extend this feature is to compute the histograms of multiple resolutions of an image to form a multiresolution histogram. The multiresolution histogram shares many desirable properties with the plain histogram including that they are both fast to compute, space efficient, invariant to rigid motions, and robust to noise. In addition, the multiresolution histogram directly encodes spatial information. We describe a simple yet novel matching algorithm based on the multiresolution histogram that uses the differences between histograms of consecutive image resolutions. We evaluate it against five widely used image features. We show that with our simple feature we achieve or exceed the performance obtained with more complicated features. Further, we show our algorithm to be the most efficient and robust.