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Featured researches published by Juwei Lu.


Information Sciences | 2014

A collaborative approach for face verification and attributes refinement

Liyan Zhang; Bradley Scott Denney; Juwei Lu

Face verification which aims to determine whether two given faces refer to the same person, and human attribute learning with the goal of extracting predefined describable attributes from face images, are two fundamental issues in a variety of applications (e.g., face tagging, attribute based face search). While advances in computer vision domain have resulted in a series of techniques for each of the two tasks, such techniques are usually prone to errors due to the large variation of faces in pose, expression, illumination, occlusion, etc. Different from most prior related works which focus on the two tasks separately, in this paper, we explore their relationships and propose a collaborative approach allowing them to interact with each other to iteratively reduce errors and uncertainties. The interaction is embodies in two processes, one is that the results of face verification can be leveraged to refine attribute values utilizing the random-walk model, and the other is that the attribute values can also be employed to improve the face verification performance through R-LDA model. The two interactive processes will continue to iteratively improve the performance of the two tasks, until the relative stable results are achieved. Experimental results on the real-world photo collections demonstrate the effectiveness of the proposed approach.


Multimedia Tools and Applications | 2016

Sub-event recognition and summarization for structured scenario photos

Liyan Zhang; Bradley Scott Denney; Juwei Lu

Structured scenario photos, referring to the images which capture important events that usually follow specific routines/structures (such as wedding ceremonies, graduation ceremonies, etc.), account for a significant proportion in personal photo collections. Conventional image analysis techniques without considering the event routines/structures are not sufficient to handle these photos. In this paper, we explore the appropriate framework to learn and utilize the specific routines for understanding these structure scenario photos. Specifically, we propose a novel framework which can systematically integrate Hidden Markov Model and Gaussian Mixture Model to recognize sub-events from structured scenario photos. Then we present a comprehensive criterion to select representative images to summarize the whole photo collection. Experimental results conducted on the real-world datasets demonstrate the superiority of our framework in both of sub-event recognition and photo summarization tasks.


Archive | 2012

SYSTEMS AND METHODS FOR IMAGE MANAGEMENT

Liyan Zhang; Bradley Scott Denney; Juwei Lu


Archive | 2013

Devices, systems, and methods for visual-attribute refinement

Liyan Zhang; Juwei Lu; Bradley Scott Denney


Archive | 2014

SYSTEMS AND METHODS FOR CREATING A SEMANTIC-DRIVEN VISUAL VOCABULARY

Bradley Scott Denney; Juwei Lu; Dariusz Dusberger; Sholeh Forouzan


Archive | 2013

SYSTEMS AND METHODS FOR FEATURE FUSION

Yang Yang; Bradley Scott Denney; Juwei Lu; Dariusz Dusberger; Hung Khei Huang


Archive | 2012

Systems and methods for creating a visual vocabulary

Juwei Lu; Bradley Scott Denney


Archive | 2012

SYSTEMS AND METHODS FOR TOPIC-SPECIFIC VIDEO PRESENTATION

Juwei Lu; Bradley Scott Denney


Archive | 2013

Systems and methods for generating a high-level visual vocabulary

Yang Yang; Bradley Scott Denney; Juwei Lu; Dariusz Dusberger; Hung Khei Huang


Archive | 2013

DEVICES, SYSTEMS, AND METHODS FOR LARGE-SCALE LINEAR DISCRIMINANT ANALYSIS OF IMAGES

Juwei Lu; Bradley Scott Denney; Hung Khei Huang

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