3 research outputs found
A Novel Image Retrieval Based on a Combination of Local and Global Histograms of Visual Words
Content-based image retrieval (CBIR) provides a sustainable solution to retrieve similar images from an image archive. In the last few years, the Bag-of-Visual-Words (BoVW) model gained attention and significantly improved the performance of image retrieval. In the standard BoVW model, an image is represented as an orderless global histogram of visual words by ignoring the spatial layout. The spatial layout of an image carries significant information that can enhance the performance of CBIR. In this paper, we are presenting a novel image representation that is based on a combination of local and global histograms of visual words. The global histogram of visual words is constructed over the whole image, while the local histogram of visual words is constructed over the local rectangular region of the image. The local histogram contains the spatial information about the salient objects. Extensive experiments and comparisons conducted on Corel-A, Caltech-256, and Ground Truth image datasets demonstrate that the proposed image representation increases the performance of image retrieval
Efficient Retrieval of Images with Irregular Patterns using Morphological Image Analysis: Applications to Industrial and Healthcare datasets
Image retrieval is the process of searching and retrieving images from a
database based on their visual content and features. Recently, much attention
has been directed towards the retrieval of irregular patterns within industrial
or medical images by extracting features from the images, such as deep
features, colour-based features, shape-based features and local features. This
has applications across a spectrum of industries, including fault inspection,
disease diagnosis, and maintenance prediction. This paper proposes an image
retrieval framework to search for images containing similar irregular patterns
by extracting a set of morphological features (DefChars) from images; the
datasets employed in this paper contain wind turbine blade images with defects,
chest computerised tomography scans with COVID-19 infection, heatsink images
with defects, and lake ice images. The proposed framework was evaluated with
different feature extraction methods (DefChars, resized raw image, local binary
pattern, and scale-invariant feature transforms) and distance metrics to
determine the most efficient parameters in terms of retrieval performance
across datasets. The retrieval results show that the proposed framework using
the DefChars and the Manhattan distance metric achieves a mean average
precision of 80% and a low standard deviation of 0.09 across classes of
irregular patterns, outperforming alternative feature-metric combinations
across all datasets. Furthermore, the low standard deviation between each class
highlights DefChars' capability for a reliable image retrieval task, even in
the presence of class imbalances or small-sized datasets.Comment: 35 pages, 5 figures, 19 tables (17 tables in appendix), submitted to
Special Issue: Advances and Challenges in Multimodal Machine Learning 2nd
Edition, Journal of Imaging, MDP