Activating Frequency and ViT for 3D Point Cloud Quality Assessment without Reference

Deep learning-based quality assessments have significantly enhanced perceptual multimedia quality assessment, however it is still in the early stages for 3D visual data such as 3D point clouds (PCs). Due to the high volume of 3D-PCs, such quantities are frequently compressed for transmission and viewing, which may affect perceived quality. Therefore, we propose no-reference quality metric of a given 3D-PC. Comparing to existing methods that mostly focus on geometry or color aspects, we propose integrating frequency magnitudes as indicator of spatial degradation patterns caused by the compression. To map the input attributes to quality score, we use a light-weight hybrid deep model; combined of Deformable Convolutional Network (DCN) and Vision Transformers (ViT). Experiments are carried out on ICIP20 [1], PointXR [2] dataset, and a new big dataset called BASICS [3]. The results show that our approach outperforms state-of-the-art NR-PCQA measures and even some FR-PCQA on PointXR. The implementation code can be found at: https://github.com/o-messai/3D-PCQA

Bag of Recurrence Patterns Representation for Time-Series Classification

Time-Series Classification (TSC) has attracted a lot of attention in pattern recognition, because wide range of applications from different domains such as finance and health informatics deal with time-series signals. Bag of Features (BoF) model has achieved a great success in TSC task by summarizing signals according to the frequencies of "feature words" of a data-learned dictionary. This paper proposes embedding the Recurrence Plots (RP), a visualization technique for analysis of dynamic systems, in the BoF model for TSC. While the traditional BoF approach extracts features from 1D signal segments, this paper uses the RP to transform time-series into 2D texture images and then applies the BoF on them. Image representation of time-series enables us to explore different visual descriptors that are not available for 1D signals and to treats TSC task as a texture recognition problem. Experimental results on the UCI time-series classification archive demonstrates a significant accuracy boost by the proposed Bag of Recurrence patterns (BoR), compared not only to the existing BoF models, but also to the state-of-the art algorithms.

Classification of Time-Series Images Using Deep Convolutional Neural Networks

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.