Comprehensive linguistic-visual composition network for image retrieval
Composing text and image for image retrieval (CTI-IR) is a new yet challenging task, for
which the input query is not the conventional image or text but a composition, ie, a reference …
which the input query is not the conventional image or text but a composition, ie, a reference …
Using machine learning for cognitive Robotic Process Automation (RPA)
P Martins, F Sá, F Morgado… - 2020 15th Iberian …, 2020 - ieeexplore.ieee.org
There are many business routine tasks and processes which are performed by qualified
resources which can be reallocated, allowing qualified workers to dedicate their effort to …
resources which can be reallocated, allowing qualified workers to dedicate their effort to …
Large-scale instance-level image retrieval
The great success of visual features learned from deep neural networks has led to a
significant effort to develop efficient and scalable technologies for image retrieval …
significant effort to develop efficient and scalable technologies for image retrieval …
Detailed investigation of deep features with sparse representation and dimensionality reduction in cbir: A comparative study
Research on content-based image retrieval (CBIR) has been under development for
decades, and numerous methods have been competing to extract the most discriminative …
decades, and numerous methods have been competing to extract the most discriminative …
OntoKnowNHS: ontology driven knowledge centric novel hybridised semantic scheme for image recommendation using knowledge graph
N Roopak, G Deepak - Knowledge Graphs and Semantic Web: Third …, 2021 - Springer
Multimedia content is increasing immensely as there are various websites available to
upload images. Image retrieval is a method of searching for, viewing, and retrieving images …
upload images. Image retrieval is a method of searching for, viewing, and retrieving images …
Reproducible experiments with learned metric index framework
This work is a companion reproducible paper of a previous paper (Antol et al., 2021) in
which we presented an alternative to the traditional paradigm of similarity searching in …
which we presented an alternative to the traditional paradigm of similarity searching in …
Data-driven learned metric index: an unsupervised approach
Metric indexes are traditionally used for organizing unstructured or complex data to speed
up similarity queries. The most widely-used indexes cluster data or divide space using hyper …
up similarity queries. The most widely-used indexes cluster data or divide space using hyper …
Deep permutations: deep convolutional neural networks and permutation-based indexing
The activation of the Deep Convolutional Neural Networks hidden layers can be
successfully used as features, often referred as Deep Features, in generic visual similarity …
successfully used as features, often referred as Deep Features, in generic visual similarity …
PPP-codes for large-scale similarity searching
Many current applications need to organize data with respect to mutual similarity between
data objects. A typical general strategy to retrieve objects similar to a given sample is to …
data objects. A typical general strategy to retrieve objects similar to a given sample is to …
Metric hull as similarity-aware operator for representing unstructured data
Similarity searching has become widely utilized in many online services processing
unstructured and complex data, eg, Google Images. Metric spaces are often applied to …
unstructured and complex data, eg, Google Images. Metric spaces are often applied to …