The content based image retrieval aims to find the similar images from a large scale dataset
against a query image. Generally, the similarity between the representative features of the …

Instance-level image retrieval is the task of searching in a large database for images that
match an object in a query image. To address this task, systems usually rely on a retrieval …

Low-level features and deep features each have their own advantages and disadvantages
in image representation. However, combining their advantages within a CBIR framework …

Deep learning techniques can be used to describe image content, which is a good way to
reduce the semantic gap between low-level and high-level features. However, convolutional …

Leaf image patterns have been actively researched for plant species recognition. However,
as a very challenging fine-grained pattern identification issue, cultivar recognition in which …

With the wide use of visual sensors in the Internet of Things (IoT) in the past decades, huge
amounts of images are captured in people's daily lives, which poses challenges to …

L2-normalization is an effective method to enhance the discriminant power of deep
representation learning. However, without exploiting the geometric properties of the feature …

Identifying the target object in an image can produce more accurate and discriminative
feature representations, which can significantly improve large-scale instance-level image …

Aggregating deep convolutional features for image retrieval has obtained excellent results in
recent years; however, exploiting the several advantages of deep convolutional feature …

Applying deep convolutional features to image retrieval has become the mainstream method
in the field of image retrieval. However, the discriminative power of deep convolutional …