Image retrieval based on deep convolutional features has demonstrated state-of-the-art
performance in popular benchmarks. In this paper, we present a unified solution to address …

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

In this paper a Convolutional Neural Network framework for Content Based Image Retrieval
is proposed. We employ a deep CNN model to obtain the feature representations from the …

Deep convolutional neural networks have been successfully applied to image classification
tasks. When these same networks have been applied to image retrieval, the assumption has …

Image Retrieval is a fundamental task of obtaining images similar to the query one from a
database. A common image retrieval practice is to firstly retrieve candidate images via …

In this paper, we propose a novel framework for instance-level image retrieval. Recent
methods focus on fine-tuning the Convolutional Neural Network (CNN) via a Siamese …

Abstract Many approaches using Convolutional Neural Network (CNN) for efficient image
retrieval have concentrated on feature aggregation rather than feature embedding over …

Despite significant progress of applying deep learning methods to the field of content-based
image retrieval, there has not been a software library that covers these methods in a unified …

Deep convolutional neural networks have demonstrated breakthrough accuracies for image
classification. A series of feature extractors learned from CNN have been used in other …

Image retrieval systems conventionally use a two-stage paradigm, leveraging global
features for initial retrieval and local features for reranking. However, the scalability of this …