, we propose a novel formulation to automatically design discriminative “category-level attributes”, which can be efficiently encoded by a compact category-attribute matrix. The formulation allows us to achieve intuitive and critical design criteria (category-separability, learnability) in a principled

, we propose a novel formulation to automatically de-sign discriminative “category-level attributes”, which can be efficiently encoded by a compact category-attribute ma-trix. The formulation allows us to achieve intuitive and crit-ical design criteria (category-separability, learnability) in a

, we propose a novel formulation to automatically de-sign discriminative “category-level attributes”, which can be efficiently encoded by a compact category-attribute ma-trix. The formulation allows us to achieve intuitive and crit-ical design criteria (category-separability, learnability) in a

This is the supplementary material for Designing Category-Level Attributes for Discriminative Visual Recognition [3]. We first provide an overview of the proposed approach in Section 1. The proof of the theorem is shown in Section 2. Additional remarks of the proposed attribute design algorithm

This is the supplementary material for Designing Category-Level Attributes for Dis-criminative Visual Recognition [3]. We first provide an overview of the proposed ap-proach in Section 1. The proof of the theorem is shown in Section 2. Additional remarks of the proposed attribute design algorithm

into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition,

... is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has

Abstruct- A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence. The most unique phenotypic feature visible in a person’s face is the detailed texture of each eye’s iris: An estimate of its statistical complexity in a

Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors

We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by (1) solv- ing for correspondences between points on the two shapes, (2) using the correspondences to estimate an aligning