English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we propose a method for image-set classification based onconvex cone models. Image set classification aims to classify a set of images,which were usually obtained from video frames or multi-view cameras, into atarget object. To accurately and stably classify a set, it is essential torepresent structural information of the set accurately. There are variousrepresentative image features, such as histogram based features, HLAC, andConvolutional Neural Network (CNN) features. We should note that most of themhave non-negativity and thus can be effectively represented by a convex cone.This leads us to introduce the convex cone representation to image-setclassification. To establish a convex cone based framework, we mathematicallydefine multiple angles between two convex cones, and then define the geometricsimilarity between the cones using the angles. Moreover, to enhance theframework, we introduce a discriminant space that maximizes the between-classvariance (gaps) and minimizes the within-class variance of the projected convexcones onto the discriminant space, similar to the Fisher discriminant analysis.Finally, the classification is performed based on the similarity betweenprojected convex cones. The effectiveness of the proposed method isdemonstrated experimentally by using five databases: CMU PIE dataset, ETH-80,CMU Motion of Body dataset, Youtube Celebrity dataset, and a private databaseof multi-view hand shapes.

Constrained Mutual Convex Cone Method for Image Set Based Recognition