Advances in Multimedia

Retracted: Enhanced P2P Services Providing Multimedia Content

This paper proposes and evaluates the LFrWF, a novel lifting-based architecture to compute the discrete wavelet transform (DWT) of images using the fractional wavelet filter (FrWF). In order to reduce the memory requirement of the proposed architecture, only one image line is read into a buffer at a time. Aside from an LFrWF version with multipliers, i.e., the LFr<span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M1" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 27.6517 11.7065" width="27.6517pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g190-88" d="M956 650H736V622C814 613 818 602 804 542C789 471 733 275 691 141H689C628 314 570 476 514 641H485C434 478 377 312 323 142H321C277 284 238 419 199 554C184 605 186 613 259 622V650H16V622C77 615 92 606 117 524C172 346 228 170 281 -11H312C363 151 419 312 475 488H477C528 343 597 150 652 -11H683C765 269 826 458 848 522C878 605 887 613 956 622V650Z"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><path id="g190-71" d="M493 503C489 551 484 614 483 650H43V622C120 616 128 611 128 525V126C128 40 120 34 40 28V0H312V28C221 34 213 40 213 126V307H316C407 307 412 296 424 227H453V420H424C412 355 407 346 316 346H213V584C213 613 216 616 246 616H322C398 616 419 607 436 579C449 559 455 539 464 499L493 503Z"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><path id="g190-110" d="M797 0V26C739 32 732 36 732 103V296C732 394 682 449 605 449C576 449 550 437 529 423C504 407 475 389 446 366C425 418 382 449 334 449C303 449 279 437 253 421C222 403 201 385 180 371V452C135 432 85 419 41 411V388C99 379 102 374 102 310V103C102 38 93 32 27 26V0H238V26C189 32 180 38 180 103V338C210 363 250 390 289 390C351 390 377 348 377 275V103C377 37 368 32 306 26V0H520V26C465 32 456 38 456 101V296C456 314 455 326 453 338C491 369 529 390 565 390C628 390 653 345 653 274V107C653 36 642 32 583 26V0H797Z"/></g></svg>,</span> we develop a multiplier-less LFrWF version, i.e., the LFr<span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M2" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 30.0083 11.7065" width="30.0083pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g><g transform="matrix(.0091,0,0,-0.0091,27.072,3.132)"><path id="g190-109" d="M238 0V26C174 32 166 38 166 104V712C132 700 70 683 18 677V653C81 647 87 645 87 577V104C87 38 78 32 15 26V0H238Z"/></g></svg>,</span> which reduces the critical path delay (CPD) to the delay <svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.29108pt" id="M3" height="12.2532pt" version="1.1" viewBox="-0.0498162 -8.96212 12.4956 12.2532" width="12.4956pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-85" d="M620 675H597C578 656 570 650 541 650H144C112 650 104 653 94 675H72C59 618 42 552 23 493L53 491C71 534 88 564 105 585C124 608 144 615 238 615H290L197 121C182 40 174 34 88 28L82 0H361L367 28C275 34 266 38 281 121L374 615H441C522 615 543 608 553 583C562 560 566 531 565 493L597 494C603 551 612 629 620 675Z"/></g><g transform="matrix(.0091,0,0,-0.0091,7.176,3.132)"><path id="g50-98" d="M490 97L476 124C442 96 405 70 398 70C392 70 390 78 396 114C419 243 448 379 463 432L457 436C446 436 431 439 418 442C393 447 368 451 343 451C281 451 204 418 155 381C74 320 24 206 24 107C24 23 59 -12 88 -12C118 -12 155 5 191 34C236 70 290 122 328 177H330L312 84C296 0 311 -12 331 -12C355 -12 425 24 490 97ZM374 387C371 367 360 299 347 264C323 202 187 53 142 53C128 53 113 73 113 120C113 224 157 332 221 380C241 395 274 403 303 403C330 403 360 395 374 387Z"/></g></svg> of an adder. The proposed LFr<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M4" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 27.6517 11.7065" width="27.6517pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g></svg> and LFr<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M5" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 30.0083 11.7065" width="30.0083pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g><g transform="matrix(.0091,0,0,-0.0091,27.072,3.132)"><use xlink:href="#g190-109"/></g></svg> architectures are compared in terms of the required adders, multipliers, memory, and critical path delay with state-of-the-art DWT architectures. Moreover, the proposed LFr<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M6" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 27.6517 11.7065" width="27.6517pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g></svg> and LFr<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M7" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 30.0083 11.7065" width="30.0083pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g><g transform="matrix(.0091,0,0,-0.0091,27.072,3.132)"><use xlink:href="#g190-109"/></g></svg> architectures, along with the state-of-the-art FrWF architectures (with multipliers (Fr<span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M8" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 27.6517 11.7065" width="27.6517pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g></svg>)</span> and without multipliers (Fr<span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M9" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 30.0083 11.7065" width="30.0083pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g><g transform="matrix(.0091,0,0,-0.0091,27.072,3.132)"><use xlink:href="#g190-109"/></g></svg>))</span> are compared through implementation on the same FPGA board. The LFr<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M10" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 27.6517 11.7065" width="27.6517pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g></svg> requires 22% less look-up tables (LUT), 34% less flip-flops (FF), and 50% less compute cycles (CC) and consumes 65% less energy than the Fr<span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M11" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 27.6517 11.7065" width="27.6517pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g></svg>.</span> Also, the proposed LFr<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M12" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 30.0083 11.7065" width="30.0083pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g><g transform="matrix(.0091,0,0,-0.0091,27.072,3.132)"><use xlink:href="#g190-109"/></g></svg> architecture requires 50% less CC and consumes 43% less energy than the Fr<span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M13" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 30.0083 11.7065" width="30.0083pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g><g transform="matrix(.0091,0,0,-0.0091,27.072,3.132)"><use xlink:href="#g190-109"/></g></svg>.</span> Thus, the proposed LFr<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M14" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 27.6517 11.7065" width="27.6517pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g></svg> and LFr<svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" style="vertical-align:-3.18152pt" id="M15" height="11.7065pt" version="1.1" viewBox="-0.0498162 -8.52498 30.0083 11.7065" width="30.0083pt"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g190-88"/></g><g transform="matrix(.013,0,0,-0.013,12.623,0)"><use xlink:href="#g190-71"/></g><g transform="matrix(.0091,0,0,-0.0091,19.573,3.132)"><use xlink:href="#g190-110"/></g><g transform="matrix(.0091,0,0,-0.0091,27.072,3.132)"><use xlink:href="#g190-109"/></g></svg> architectures appear suitable for computing the DWT of images on wearable sensors.

Mohd Tausif

Ekram Khan

Mohd Hasan

Martin Reisslein

Lifting-Based Fractional Wavelet Filter: Energy-Efficient DWT Architecture for Low-Cost Wearable Sensors

This study attempts to predict stock index prices using multivariate time series analysis. The study&#x2019;s motivation is based on the notion that datasets of stock index prices involve weak periodic patterns, long-term and short-term information, for which traditional approaches and current neural networks such as Autoregressive models and Support Vector Machine (SVM) may fail. This study applied Temporal Pattern Attention and Long-Short-Term Memory (TPA-LSTM) for prediction to overcome the issue. The results show that stock index prices prediction through the TPA-LSTM algorithm could achieve better prediction performance over traditional deep neural networks, such as recurrent neural network (RNN), convolutional neural network (CNN), and long and short-term time series network (LSTNet).

Xiaolu Wei

Binbin Lei

Hongbing Ouyang

Qiufeng Wu

Stock Index Prices Prediction via Temporal Pattern Attention and Long-Short-Term Memory

Recognition of human emotion from facial expression is affected by distortions of pictorial quality and facial pose, which is often ignored by traditional video emotion recognition methods. On the other hand, context information can also provide different degrees of extra clues, which can further improve the recognition accuracy. In this paper, we first build a video dataset with seven categories of human emotion, named human emotion in the video (HEIV). With the HEIV dataset, we trained a context-aware attention network (CAAN) to recognize human emotion. The network consists of two subnetworks to process both face and context information. Features from facial expression and context clues are fused to represent the emotion of video frames, which will be then passed through an attention network and generate emotion scores. Then, the emotion features of all frames will be aggregated according to their emotional score. Experimental results show that our proposed method is effective on HEIV dataset.

Xiaodong Liu

Miao Wang

Context-Aware Attention Network for Human Emotion Recognition in Video

In this paper, a symmetry detection algorithm for three-dimensional point cloud model based on weighted principal component analysis (PCA) is proposed. The proposed algorithm works as follows: first, using the point element&#x2019;s area as the initial weight, a weighted PCA is performed and a plane is selected as the initial symmetry plane; and then an iterative method is used to adjust the approximate symmetry plane step by step to make it tend to perfect symmetry plane (dominant symmetry plane). In each iteration, we first update the weight of each point based on a distance metric and then use the new weights to perform a weighted PCA to determine a new symmetry plane. If the current plane of symmetry is close enough to the plane of symmetry in the previous iteration or if the number of iterations exceeds a given threshold, the iteration terminates. After the iteration is terminated, the plane of symmetry in the last iteration is taken as the dominant symmetry plane of the model. As shown in experimental results, the proposed algorithm can find the dominant symmetry plane for symmetric models and it also works well for nonperfectly symmetric models.

Chen He

Lei Wang

Yonghui Zhang

Chunmeng Wang

Dominant Symmetry Plane Detection for Point-Based 3D Models

Digital image watermarking is an attractive research area since it protects the multimedia data from unauthorized access. For designing an efficient and robust digital image watermarking system, the trade-off among imperceptibility, robustness, capacity, and security must be maintained. Various studies regarding this concern have been performed to ensure these requirements by hybridizing different domains, such as spatial and transform domains. In this paper, we have presented an analytical study of the existing hybrid digital image watermarking methods. At first, we have given a standard framework for designing a hybrid method that ensures the basic design requirements of watermarking for various applications. After a brief literature review, we compared and analyzed the complexity of several existing hybrid methods in a tabular form. The limitations and applications of these methods are also highlighted. Finally, we summarized the challenges of the existing methods and concluded the study by giving future research directions.

Mahbuba Begum

Mohammad Shorif Uddin

Analysis of Digital Image Watermarking Techniques through Hybrid Methods

While the reconstruction of 3D objects is increasingly used today, the simplification of 3D point cloud, however, becomes a substantial phase in this process of reconstruction. This is due to the huge amounts of dense 3D point cloud produced by 3D scanning devices. In this paper, a new approach is proposed to simplify 3D point cloud based on <i>k</i>-nearest neighbor (<i>k</i>-NN) and clustering algorithm. Initially, 3D point cloud is divided into clusters using <i>k</i>-means algorithm. Then, an entropy estimation is performed for each cluster to remove the ones that have minimal entropy. In this paper, MATLAB is used to carry out the simulation, and the performance of our method is testified by test dataset. Numerous experiments demonstrate the effectiveness of the proposed simplification method of 3D point cloud.

Abdelaaziz Mahdaoui

El Hassan Sbai

3D Point Cloud Simplification Based on <i>k</i>-Nearest Neighbor and Clustering

In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. Our framework consists of the key-frame extraction algorithm and the feature aggregation strategy. Specifically, the key-frame extraction algorithm takes advantage of the clustering idea so that redundant information is removed in video data and storage cost is greatly reduced. The feature aggregation strategy adopts average pooling to encode deep local convolutional features followed by coarse-to-fine retrieval, which allows rapid retrieval in the large-scale video database. The results from extensive experiments on two publicly available datasets demonstrate that the proposed method achieves superior efficiency as well as accuracy over other state-of-the-art visual search methods.

Chen Zhang

Bin Hu

Yucong Suo

Zhiqiang Zou

Yimu Ji

Large-Scale Video Retrieval via Deep Local Convolutional Features

Grass is one of the most important resources in the ecosystem for the sustainable development of human beings. However, the studies focusing on grass identification, which were traditionally implemented by experts with low efficiency and precision, cannot meet the requirements of modern grassland management. In this study, we proposed cubic interpolation LBP (CILBP) and dbN wavelets for grass identification based on leaf images. A low-frequency component of leaf images decomposed by dbN wavelets was used as the input of CILBP for more subtle texture extraction. The novelty of the proposed method was that CILBP can better describe the texture features from the low-frequency subimage, as compared with the original bilinear LBP. The effectiveness in identification accuracy of the proposed method for grass leaf was demonstrated by the experimental results.

Fan Han

Xue Qiao

Yubao Ma

Weihong Yan

Xinyu Wang

Xin Pan

Grass Leaf Identification Using dbN Wavelet and CILBP

This paper presents a new multisupervised coupled metric learning (MS-CML) method for low-resolution face image matching. While coupled metric learning has achieved good performance in degraded face recognition, most existing coupled metric learning methods only adopt the category label as supervision, which easily leads to changes in the distribution of samples in the coupled space. And the accuracy of degraded image matching is seriously influenced by these changes. To address this problem, we propose an MS-CML method to train the linear and nonlinear metric model, respectively, which can project the different resolution face pairs into the same latent feature space, under which the distance of each positive pair is reduced and that of each negative pair is enlarged. In this work, we defined a novel multisupervised objective function, which consists of a main objective function and an auxiliary objective function. The supervised information of the main objective function is the category label, which plays a major supervisory role. The supervised information of the auxiliary objective function is the distribution relationship of the samples, which plays an auxiliary supervisory role. Under the supervision of category label and distribution information, the learned model can better deal with the intraclass multimodal problem, and the features obtained in the coupled space are more easily matched correctly. Experimental results on three different face datasets validate the efficacy of the proposed method.

Guofeng Zou

Guixia Fu

Xiang Peng

A Novel Multisupervised Coupled Metric Learning for Low-Resolution Face Matching

Pedestrian detection and classification are of increased interest in the intelligent transportation system (ITS), and among the challenging issues, we can find limitations of tiny and occluded appearances, large variation of human pose, cluttered background, and complex environment. In fact, a partial occlusion handling is important in the case of detecting pedestrians, in order to avoid accidents between pedestrians and vehicles, since it is difficult to detect when pedestrians are suddenly crossing the road. To solve the partial occlusion problem, a pyramidal part-based model (PPM) is proposed to obtain a more accurate prediction based on the majority vote of the confidence score of the visible parts by cascading the pyramidal structure. The experimental results on the proposed scheme achieved 96.25&#x0025; accuracy on the INRIA dataset and 81&#x0025; accuracy on the PSU (Prince of Songkla University) dataset. Our proposed model can be applied in the real-world environment to classify the occluded part of pedestrians with the various information of part representation at each pyramid layer.

M. Thu

N. Suvonvorn

Pyramidal Part-Based Model for Partial Occlusion Handling in Pedestrian Classification

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<p><em>Advances in Multimedia</em> publishes research on the technologies associated with multimedia systems, including computer-media integration for digital information processing, storage, transmission, and representation.</p>


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Advances in Multimedia - Table of contents 2020

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Advances in Multimedia publishes research on the technologies associated with multimedia systems, including computer-media integration for digital information processing, storage, transmission, and representation.

Advances in Multimedia