News

In Summer 2021, we have a new MCL member, Xinyu Wang, joining our big family. Here is a short interview with Xinyu with our great welcome.

1. Could you briefly introduce yourself and your research interests?

My name is Xinyu Wang, I am a Master student in Electrical Engineering, and it’s my second year at USC. I am new here as a summer intern. My research interests mainly include machine learning and robotics, and I will work on image forensics related topics this summer at MCL.

2. Could you briefly introduce yourself and your research interests?

MCL has a group of motivated and intelligent people, who are full of passion about their research. And I am impressed by the open and friendly atmosphere here. People are encouraged to show their ideas and help each other, and everyone is friendly and supportive.

3. What is your future expectation and plan in MCL?

This summer, I am working with Yao on image forensics topics using the green learning method, under the supervision of Professor Kuo. I believe this will be a great opportunity for me to further explore machine learning and working on this interesting topic. I also hope to make new friends here and make lasting connections with MCL members.

Image anomaly localization is an important problem in image processing and computer vision, with numerous applications in many areas, such as industrial manufacturing inspection, medical image diagnosis and even video surveillance analysis. The goal of image anomaly localization is to locate the anomaly or anomalous region on the pixel level. Like most other anomaly detection problems, we formulate image anomaly localization as an unsupervised task. More specifically, it means training set only contains normal images, and no anomalous images and corresponding labeled masks are available during model training. This is because anomalous examples are either too expensive to collect or too few to be modeled, which makes it an extremely challenging yet attracting problem.

To tackle this problem, we propose a new image anomaly localization method, called AnomalyHop [1], based on the successive subspace learning (SSL) framework. This is also the first work that applies SSL to the anomaly localization problem. AnomalyHop consists of three modules: 1) feature extraction via successive subspace learning (SSL), 2) normality feature distributions modeling via various Gaussian models, and 3) anomaly map generation and fusion. As compared with previous deep-learning-based image anomaly localization methods, AnomalyHop is mathematically transparent, easy to train, and fast in its inference speed. Besides that, its area under the ROC curve (ROC-AUC) performance on the MVTec AD dataset is 95.9%, which is the state-of-the-art performance.

-By Kaitai Zhang and Bin Wang

[1] Zhang, K., Wang, B., Wang, W., Sohrab, F., Gabbouj, M., & Kuo, C. C. J. (2021). AnomalyHop: An SSL-based Image Anomaly Localization Method. arXiv preprint arXiv:2105.03797.

Image super-resolution (SR) is a classic image reconstruction problem in computer vision (CV), which aims at recovering a high-resolution image from a low-resolution image. As a type of supervised generative problem, image SR attracts wide attention due to its strong connection with other CV topics, such as object recognition, object alignment, texture synthesis and so on. Besides, it has extensive applications in real world, for example, medical diagnosis, remote sensing, biometric information identification, etc.

For the state-of-the-art approaches for SR, typically there are two mainstreams: 1) example-based learning methods, and 2) Deep Learning (CNN-based) methods. Example-based methods either exploit external low-high resolution exemplar pairs [1], or learn internal similarity of the same image with different resolution scales [2]. However, features used in example-based methods are usually traditional gradient-related or just handcraft, which may affect model performance. While CNN-based SR methods (e.g. SRCNN [3]) does not really distinguish between feature extraction and decision making. Lots of basic CNN models/blocks are applied to SR problem, e.g. GAN, residual learning, attention network, and provide superior SR results. Nevertheless, the non-explainable process and exhaustive training cost are serious drawbacks of CNN-based methods.

We propose a Successive-Subspace-Learning-based (SSL-based) method to gradually partition data into subspaces by feature statistics. In addition, we utilize spatial-spectral compatible cw-Saab features to express exemplar pairs by taking advantage of reasonable feature extraction [4]. In the future, we aim at providing such a SSL-based explainable method with high efficiency for SR problem.

—  By Wei Wang

Reference:

[1] Timofte, Radu, Vincent De Smet, and Luc Van Gool. “A+: Adjusted anchored neighborhood regression for fast super-resolution.” Asian conference on computer vision. Springer, Cham, 2014.

[2] Huang, Jia-Bin, Abhishek Singh, and Narendra Ahuja. “Single image super-resolution from transformed self-exemplars.” Proceedings of the IEEE conference on [...]

Congratulations to MCL Director, Professor C.-C. Jay Kuo, for being selected as the recipient of the 2021 IEEE CASS Charles A. Desoer Technical Achievement Award. This award is named after  Charles A. Desoer, Professor of Electrical Engineering and Computer Science, Emeritus, at UC Berkeley. This award honors individuals whose exceptional technical contributions to a field within the scope of the Circuits and Systems Society have been consistently evident over a period of years. Contributions are documented by publications and based on originality and continuity of effort. Professor Kuo received this award for his contributions to visual communications and multimedia systems

The 2021 CAS Society Awards Ceremony will be held virtually in parallel with the ISCAS 2021 event on Monday, 24 May. The ceremony will be pre-recorded and presented on both the online platform and at the live banquet in Daegu. Here is Professor Kuo’s acceptance speech.

“It is my great honor to receive the 2021 IEEE CASS Charles A. Desoer Technical Achievement Award. I know that this is a highly competitive award, and there are many well qualified nominees each year. I would like to give my deepest appreciation to the Technical Achievement Award Sub-Committee and the CASS Board for their recognition.

I am also grateful for the excellent research environment provided by the University of Southern California. It is a privilege to supervise 160 hardworking PhD students at USC in the last 30 years. We brainstorm research ideas, share research frustration, and enjoy research breakthrough together. I can say that there is nothing more rewarding than working with a large number of talented young people.

I have been heavily involved in two CASS technical committees in my career. They are the visual signal processing and communication technical [...]

Block-based image coding is adopted by the JPEG standard, which has been widely used in the last three decades. The block-based discrete Cosine transform (DCT) and quantization matrices in YCbCr three color channels play key roles in JPEG. In this work, we propose a new image coding method, called DCST. It adopts data-driven colour transform and spatial transforms based on statistical properties of image pixels and machine learning. To match the data-driven forward transform, we propose a quantization table based on the human visual system (HVS). Furthermore, to efficiently compensate for the quantization error, a machine learning-based inverse transform is used. The performance of our new design is verified using the Kodak image dataset. The optimal inverse transformation can achieve 0.11-0.30dB over the standard JPEG over a wide range of quality factors. The whole pipeline outperforms JPEG with a gain of 0.5738 in the BD-PSNR (or a decrease of 9.5713 in the BD-rate) from 0.2 to 3bpp.

the colour input: previous standard use YCbCr as input which is ideal if viewed from statics while not optimal for every single image. In our case, we trained a PCA for every single image to perform color transformation which gives better de-correlate performance;
(2D)^2 PCA [1] transformation and corresponding quantization matrix designed based on the PCA kernel and HVS [2];
Machine learning inverse transform: which uses linear regression to compute the optimal inverse transform kernel for both color conversion and spatial to spectrum transform. This idea helps to estimate the quantization error and results from a better inverse result. Comparing with the previous method which compensates this error using probability mode during the post-processing stage. The ML-inverse transformation would take less time during the decoding [...]