317 research outputs found
Quality Classified Image Analysis with Application to Face Detection and Recognition
Motion blur, out of focus, insufficient spatial resolution, lossy compression
and many other factors can all cause an image to have poor quality. However,
image quality is a largely ignored issue in traditional pattern recognition
literature. In this paper, we use face detection and recognition as case
studies to show that image quality is an essential factor which will affect the
performances of traditional algorithms. We demonstrated that it is not the
image quality itself that is the most important, but rather the quality of the
images in the training set should have similar quality as those in the testing
set. To handle real-world application scenarios where images with different
kinds and severities of degradation can be presented to the system, we have
developed a quality classified image analysis framework to deal with images of
mixed qualities adaptively. We use deep neural networks first to classify
images based on their quality classes and then design a separate face detector
and recognizer for images in each quality class. We will present experimental
results to show that our quality classified framework can accurately classify
images based on the type and severity of image degradations and can
significantly boost the performances of state-of-the-art face detector and
recognizer in dealing with image datasets containing mixed quality images.Comment: 6 page
The least eigenvalue of the complements of graphs with given connectivity
The least eigenvalue of a graph is the least eigenvalue of adjacency
matrix of . In this paper we determine the graphs which attain the minimum
least eigenvalue among all complements of connected simple graphs with given
connectivity.Comment: 10 pages. arXiv admin note: substantial text overlap with
arXiv:2209.0569
Aesthetically Relevant Image Captioning
Image aesthetic quality assessment (AQA) aims to assign numerical aesthetic
ratings to images whilst image aesthetic captioning (IAC) aims to generate
textual descriptions of the aesthetic aspects of images. In this paper, we
study image AQA and IAC together and present a new IAC method termed
Aesthetically Relevant Image Captioning (ARIC). Based on the observation that
most textual comments of an image are about objects and their interactions
rather than aspects of aesthetics, we first introduce the concept of Aesthetic
Relevance Score (ARS) of a sentence and have developed a model to automatically
label a sentence with its ARS. We then use the ARS to design the ARIC model
which includes an ARS weighted IAC loss function and an ARS based diverse
aesthetic caption selector (DACS). We present extensive experimental results to
show the soundness of the ARS concept and the effectiveness of the ARIC model
by demonstrating that texts with higher ARS's can predict the aesthetic ratings
more accurately and that the new ARIC model can generate more accurate,
aesthetically more relevant and more diverse image captions. Furthermore, a
large new research database containing 510K images with over 5 million comments
and 350K aesthetic scores, and code for implementing ARIC are available at
https://github.com/PengZai/ARIC.Comment: Aceepted by AAAI2023. Code and results available at
https://github.com/PengZai/ARI
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