Feature extraction is vital for face recognition. In this paper, we focus on the general feature
extraction framework for robust face recognition. We collect about 300 papers regarding face …

We present a novel technique for image set based face/object recognition, where each
gallery and query example contains a face/object image set captured from different …

In this paper, we propose an adaptive spatial pooling method for enhancing the
discriminability of feature representation for image classification. The core idea is to adopt a …

Generally, there is a certain correlation between the level of higher education and the public
mental health of college students. Traditionally, questionnaires and literature research …

Bag-of-Deep-Visual-Words (BoDVW) model has shown its advantage over Convolutional
Neural Network (CNN) model in image classification tasks with a small number of training …

Spatial pooling is an important step in computer vision systems like Convolutional Neural
Networks or the Bag-of-Words method. The spatial pooling purpose is to combine …

Robust local feature matching plays an important role in the challenging task of logo image
recognition. Most traditional methods consider the individual local feature but ignore the …

In the problem where there is not enough data to use Deep Learning, Bag-of-Visual-Words
(BoVW) is still a good alternative for image classification. In BoVW model, many pooling …

Feature coding and pooling are two critical stages in the widely used Bag-of-Features (BOF)
framework in image classification. After coding, each local feature formulates its …

Encoding local features of images (also known as feature coding) is a key issue in computer
vision and pattern recognition and essential to many visual tasks such as object/scene …