MCL Releases VideoSet in IEEE DataPort

The MCL joined a collaborative project to build a large-scale subjective video quality database. The database was proposed to boost a major breakthrough in video coding and processing. It consists of 220 5-second sequences in four resolutions (i.e., 1920×1080, 1280×720, 960×540 and 640×360). For each of the 880 video clips, we encoded it using the H.264 codec and conducted a large-scale subjective test on the perceptual quality. The dataset is called the “VideoSet”, which is an acronym for “Video Subject Evaluation Test (SET)”. The database are available to the public in the IEEE DataPort.

IEEE DataPort is a valuable and universally accessible repository of datasets serving the growing data needs in both research and industry. The repository is designed to accept all types of datasets, including Big Data datasets up to 2TB, and it provides both downloading capabilities and access to Cloud services to enable data analysis in the Cloud.

We appreciate the help from Dr. K. J. Ray Liu and Melissa Handa in hosting the VidoeSet database.

By |January 8th, 2017|News|Comments Off on MCL Releases VideoSet in IEEE DataPort|

Happy New Year – 2017

2016 has been a fruitful year for MCL. Some members graduated with impressive research work and began a new chapter of life. Some new students joined the MCL family and explored the joy of research. MCL members have made great efforts on their research and published quality research papers on top journals and conferences.

Wish all MCL members a happy new year.

Image credits: Photo 1: “Happy New Year” by maf04, used under CC BY-SA 2.0 / Resized with white padding on the borders; Photo 2: “New Years 2017” by maf04, used under CC BY-SA 2.0 / Resized with black padding on the borders.

By |January 1st, 2017|News|Comments Off on Happy New Year – 2017|

Congratulations to MCL Members for Passing Screening Exams

MCL members, Heming Zhang, Junting Zhang and Yueru Chen passed the screening exam in Fall 2016. The screening exam is an important examination during PhD study, which aims to estimate the research potential of the student. In the area of Signal and Image Processing, the screening exam includes four topics: applied linear algebra, digital signal processing, probability and random processes.

Congratulations to Heming, Junting and Yueru. We wish them all the best in their future research and PhD study.

By |December 25th, 2016|News|Comments Off on Congratulations to MCL Members for Passing Screening Exams|

Congratulations to Shangwen Li for Passing PhD Defense

Congratulations to Shangwen Li for passing his defense on December 1, 2016. His Ph.D. thesis is entitled “Multimodal Image Retrieval and Object Classification Using Deep Learning Features”.

Abstract of thesis:

Computer vision has achieved a major breakthrough in recent years with the advancement of deep learning based methods. However, its performance is still yet to be claimed as robust for practical applications, and more advanced methods on top of deep learning architecture are needed. This work targets at using deep learning features to tackle two major computer vision problems: Multimodal Image Retrieval and Object Classification.

Multimodal Image Retrieval (MIR) aims at building the alignment between the visual and textual modalities, thus reduce the well-known “semantic gap” in image retrieval problem. As the most widely existing textual information of images, tag plays an important semantic role in MIR framework. However, treating all tags in an image as equally important may result in misalignment between visual and textual domains, leading to bad retrieval performance. To address this problem and build a robust retrieval system, we propose an MIR framework that embeds tag importance as the textual feature. In the first part, we propose an MIR system, called Multimodal Image Retrieval with Tag Importance Prediction (MIR/TIP), to embed the automatically predicted object tag importance in image retrieval. To achieve this goal, a discounted probability metric is first presented to measure the object tag importance from human sentence descriptions. Using this as ground truth, a structured object tag importance prediction model is proposed. The proposed model integrates visual, semantic, and context cues to achieve robust object tag importance prediction performance. Our experimental results demonstrate that, by embedding the predicted object tag importance, significant performance gain can be obtained in terms of [...]

By |December 18th, 2016|News|Comments Off on Congratulations to Shangwen Li for Passing PhD Defense|

Congratulations to Chen Chen for Passing PhD Defense

Congratulations to Chen Chen for passing his defense on November 30, 2016. His Ph.D. thesis is entitled “Large-Scale Scene Classification Using Machine Learning Techniques”.

Abstract of thesis:

This thesis focuses on solving a general “scene image classification” problem using advanced machine learning techniques. Indoor/outdoor classification, outdoor background semantic segmentation for image classification, and exploration of scene class/image confusion are studied in the thesis. Unlike traditional solutions to the long-standing problems, the research works proposed several innovative machine learning-based approach to resolve the challenges from different angles. Most of the proposed approaches provide the state-of-the-art performance which are highly appreciated in corresponding field of research.

We are so glad to have him share his Ph.D. experience with us. Here is his sharing.

Ph.D. experience:

Having a Ph.D. is definitely not an easy process. I was lucky to have Prof. Kuo as my advisor/leader/team member in this process. He cared me as child of his own and provided huge amount patience and love in this process. So the PhD study was not as hard as I had heard from other friends of mine. Besides, I learned to be humble, diligent, caring and taking responsibility in this process. I acknowledge my lab mates, my wife and my cats for the lasting support in my life to gain such a huge transformation in my life.

Congratulations again to Chen and we wish him all the best in his career in Facebook.

By |December 11th, 2016|News|Comments Off on Congratulations to Chen Chen for Passing PhD Defense|

MCL Celebrated Thanksgiving Holiday

Thanksgiving luncheon, which has always been a tradition of MCL, was held on December 3, 2016. More than 30 MCL members and their families attended the luncheon. The atmosphere was joyful and we enjoyed the food and conversation with each other. “It has been a long time that I haven’t tasted good Chinese food since I arrived in LA. I would like to thank Professor Kuo for treating us to such a good luncheon,” said Professor Xin Zhou. “The atmosphere is so joyful. I enjoyed the casual chats with others,” said Anubhuti.

Though most of the MCL members are international students and did not get to spend Thanksgiving with their families. We were so happy to celebrate Thanksgiving with other MCL members as a big family.

By |December 7th, 2016|News|Comments Off on MCL Celebrated Thanksgiving Holiday|

Congratulations to Jian Li for Passing PhD Defense

Congratulations to Jian Li for passing his defense on November 29, 2016. His Ph.D. thesis is entitled “Advanced Techniques for Latent Fingerprint Enhancement and Recognition”. Here is an abstract of his thesis and we are so glad to have him share his Ph.D. experience as well.


Fingerprints provide one of the most popular biometric data, and have been widely used in individual person identification and verification. The Automated Fingerprint Identification System (AFIS) offers important evidences for criminal investigation, and serves as an important tool for law enforcement. As compared with conventional exemplar fingerprints, latent fingerprints are typically collected in a crime scene. They are often degraded and corrupted with very poor quality, leading to very low identification rates. In a practical system, a latent fingerprint has to be enhanced prior to feature extraction to ensure a reliable fingerprint matching performance. In this research, we study techniques for latent fingerprint enhancement and orientation field estimation to achieve a higher matching rate. Our studies include traditional image processing techniques as well as a new method based on the emerging convolutional neural network (CNN).

First, we propose a new method using the Markov random field (MRF) model and the sparse representation (SR) of ridges to enhance latent fingerprint. The proposed MRF-SR method is inspired by the recent success of dictionary-based methodologies in recent fingerprint community. Second, we extend the MRF-SR method in order to extract the orientation field of fingerprint. As an essential feature of fingerprints, the orientation field can enhance a fingerprint image with directional and contextual filtering. It provides a valuable supplementary tool to other orientation estimation algorithms in the literature. Finally, we explore the feasibility and study the performance of applying the CNN to latent fingerprint [...]

By |December 4th, 2016|News|Comments Off on Congratulations to Jian Li for Passing PhD Defense|

MCL Members Presented Papers at ACCV 2016

MCL members, Yuzhuo Ren and Wenchao Zheng presented their papers at Asian Conference on Computer Vision (ACCV) 2016.

Yuzhuo’s presented paper is entitled “a coarse-to-fine indoor layout estimation (CFILE) method”. Here is an abstract of the paper:

Existing solutions to indoor layout estimation problem largely rely on hand-craft features and vanishing lines, and they often fail in highly cluttered indoor rooms. The proposed coarse-to-fine indoor layout estimation (CFILE) method consists of two stages: 1) coarse layout estimation; and 2) fine layout localization. In the first stage, we adopt a fully convolutional neural network (FCN) to obtain a coarse-scale room layout estimate that is close to the ground truth globally. The proposed FCN considers combines the layout contour property and the surface property so as to provide a robust estimate in the presence of cluttered objects. In the second stage, we formulate an optimization framework that enforces several constraints such as layout contour straightness, surface smoothness and geometric constraints for layout detail refinement. Our proposed system offers the state-of-the-art performance on two commonly used benchmark datasets.

Wenchao’s paper is entitled “Object Boundary Guided Semantic Segmentation”. Here is a brief summary of the paper:

Recent development in fully-convolutional neural network (FCN) has enabled accurate pixel-level in semantic segmentation. However, segmentation details are lost since the object boundary information is not exploited. Our proposed object boundary guided FCN (OBG-FCN) is able to integrate the distinct properties of object shape and class features elegantly in a fully convolutional way with a designed masking architecture. We show that the end-to-end trainable OBG-FCN system offers great improvement in optimizing the target semantic segmentation quality.

Congratulations to Yuzhuo and Wenchao for their quality research work!

By |November 30th, 2016|News|Comments Off on MCL Members Presented Papers at ACCV 2016|

Welcome New MCL Member Yuewei Na

We are so happy to welcome a new graduate member of MCL, Yuewei Na, in Fall 2016. Let’s give him a warm welcome. Here is a short interview with him.

1. Could you briefly introduce yourself? (Previous research experience, project experience, research interest and expertise)

Before coming to USC, I graduated from Xiamen University majoring computer science. I researched machine learning and low-level vision at Xiamen University. I also have experience in building fraud detection system for e-commerce company using Spark.

2. What’s your first impression of USC and MCL?

USC is very similar to Xiamen University, both of which are beautiful and good places to study. MCL is like a big warming family. I was deeply impressed by Prof. Kuo’s understanding on various topics in research.

3. What’s your future expectation for MCL?

Hope MCL can continuously produce ideas that are influential to the research community or even the whole society in the future. I’m glad to contribute my effort to this goal.


By |November 27th, 2016|News|Comments Off on Welcome New MCL Member Yuewei Na|

A Large-Scale Subjective Video Quality Database

The MCL joined a collaborative project to build a large-scale subjective video quality database. The database was proposed to boost a major breakthrough in video coding and processing. On one hand, revolutionary ideas rather than fine-tuning patches are highly expected to accommodate increasing video traffic. On the other hand, PSNR has been the dominant distortion metric for many years, but it has also been criticized for not correlating well with perceptual quality. With this database, perceptual coding is promising to lead to numerous R&D opportunities and revolutionary research with machine learning tools.

The database consists of 200 raw sequences with a duration of 5 seconds, encoded by H.264/AVC with fixed QP as bit rate control method. They are available in 5 resolutions from 3840×2160 to 540×360. Around 1000 students participated in the subjective test and it took around 7000 hours to get sufficient samples about 3 JND points. The database will be freely available for downloading for scientific purposes.

The projected was supported by 4 major multimedia companies, Netflix, Huawei, MediaTek, and Samsung. Meanwhile, 6 universities at Shenzhen joined the project, Shenzhen Institutes of Advanced Technology (Chinese Academy of Science), Shenzhen University, Graduate School at Shenzhen (Tsinghua University), Peking University Shenzhen Graduate School, The Chinese University of Hong Kong (Shenzhen), City University of Hong Kong.

We would like to give special thanks to the participating companies, institutes and universities.

By |November 20th, 2016|News|Comments Off on A Large-Scale Subjective Video Quality Database|