In visual recognition, the images are frequently modeled as sets of local features (bags). We
show that bag of words, a common method to handle such cases, can be viewed as a …

Many modern visual recognition algorithms incorporate a step of spatial 'pooling', where the
outputs of several nearby feature detectors are combined into a local or global 'bag of …

The design of low-level image features is critical for computer vision algorithms. Orientation
histograms, such as those in SIFT~\cite {Lowe2004Distinctive} and HOG~\cite …

Discriminative learning is challenging when examples are sets of features, and the sets vary
in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods …

The Fisher kernel (FK) is a generic framework which combines the benefits of generative
and discriminative approaches. In the context of image classification the FK was shown to …

As the commonly used representation of a feature-rich data object has evolved from a single
feature vector to a set of feature vectors, a key challenge in building a content-based search …

In this work, we introduce a hierarchical matching framework with so-called side information
for image classification based on bag-of-words representation. Each image is expressed as …

A standard approach to describe an image for classification and retrieval purposes is to
extract a set of local patch descriptors, encode them into a high dimensional vector and pool …

Abstract Within Convolutional Neural Network (CNN), the convolution operations are good
at extracting local features but experience difficulty to capture global representations. Within …

In object recognition, the Bag-of-Words model assumes: i) extraction of local descriptors
from images, ii) embedding the descriptors by a coder to a given visual vocabulary space …