Deep image features sensing with multilevel fusion for complex convolution neural networks & cross domain benchmarks.

ABSTRACT: Efficient image retrieval from a variety of datasets is crucial in today's digital world. Visual properties are represented using primitive image signatures in Content Based Image Retrieval (CBIR). Feature vectors are employed to classify images into predefined categories. This research presents a unique feature identification technique based on suppression to locate interest points by computing productive sum of pixel derivatives by computing the differentials for corner scores. Scale space interpolation is applied to define interest points by combining color features from spatially ordered L2 normalized coefficients with shape and object information. Object based feature vectors are formed using high variance coefficients to reduce the complexity and are converted into bag-of-visual-word

SUBMITTER: Shabir A 

PROVIDER: S-EPMC11918433 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature