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Found 10 papers, 0 papers with code
Convex Class Model on Symmetric Positive Definite Manifolds
Our proposed framework, named Manifold Convex Class Model, represents each class on SPD manifolds using a convex model, and classification can be performed by computing distances to the convex models.
Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks
We show that existing adversarial perturbation methods are not effective to perform such an attack, especially when there are multiple faces in the input image.
What is the Best Way for Extracting Meaningful Attributes from Pictures?
With this metric, automatic quantitative evaluation can be performed on the attribute sets; thus, reducing the enormous effort to perform manual evaluation.
Determining the best attributes for surveillance video keywords generation
In this paper, we propose a novel approach, based on the shared structure exhibited amongst meaningful attributes, that enables us to compare between different automatic attribute discovery approaches. We then validate our approach by comparing various attribute discovery methods such as PiCoDeS on two attribute datasets.
Automatic and Quantitative evaluation of attribute discovery methods
In our evaluation, we gleaned some insights that could be beneficial in developing automatic attribute discovery methods to generate meaningful attributes.
Matching Image Sets via Adaptive Multi Convex Hull
We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set.
Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces
We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold.
Classification of Human Epithelial Type 2 Cell Indirect Immunofluoresence Images via Codebook Based Descriptors
In this paper, we propose a cell classification system comprised of a dual-region codebook-based descriptor, combined with the Nearest Convex Hull Classifier.
Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition
In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence.
Video Face Matching using Subset Selection and Clustering of Probabilistic Multi-Region Histograms
This paper presents a video face recognition system based on probabilistic Multi-Region Histograms to characterise performance trade-offs in: (i) selecting a subset of faces compared to using all faces, and (ii) combining information from all faces via clustering.
