6,556 research outputs found
RBIR Based on Signature Graph
This paper approaches the image retrieval system on the base of visual
features local region RBIR (region-based image retrieval). First of all, the
paper presents a method for extracting the interest points based on
Harris-Laplace to create the feature region of the image. Next, in order to
reduce the storage space and speed up query image, the paper builds the binary
signature structure to describe the visual content of image. Based on the
image's binary signature, the paper builds the SG (signature graph) to classify
and store image's binary signatures. Since then, the paper builds the image
retrieval algorithm on SG through the similar measure EMD (earth mover's
distance) between the image's binary signatures. Last but not least, the paper
gives an image retrieval model RBIR, experiments and assesses the image
retrieval method on Corel image database over 10,000 images.Comment: 4 pages, 4 figure
Compact Hash Codes for Efficient Visual Descriptors Retrieval in Large Scale Databases
In this paper we present an efficient method for visual descriptors retrieval
based on compact hash codes computed using a multiple k-means assignment. The
method has been applied to the problem of approximate nearest neighbor (ANN)
search of local and global visual content descriptors, and it has been tested
on different datasets: three large scale public datasets of up to one billion
descriptors (BIGANN) and, supported by recent progress in convolutional neural
networks (CNNs), also on the CIFAR-10 and MNIST datasets. Experimental results
show that, despite its simplicity, the proposed method obtains a very high
performance that makes it superior to more complex state-of-the-art methods
Neural network-based shape retrieval using moment invariants and Zernike moments.
Shape is one of the fundamental image features for use in Content-Based Image Retrieval (CBIR). Compared with other visual features such as color and texture, it is extremely powerful and provides capability for object recognition and similarity-based image retrieval. In this thesis, we propose a Neural Network-Based Shape Retrieval System using Moment Invariants and Zernike Moments. Moment Invariants and Zernike Moments are two region-based shape representation schemes and are derived from the shape in an image and serve as image features. k means clustering is used to group similar images in an image collection into k clusters whereas Neural Network is used to facilitate retrieval against a given query image. Neural Network is trained by the clustering result on all of the images in the collection using back-propagation algorithm. In this scheme, Neural Network serves as a classifier such that moments are inputs to the Neural Network and the output is one of the k classes that have the largest similarities to the query image. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .C444. Source: Masters Abstracts International, Volume: 44-03, page: 1396. Thesis (M.Sc.)--University of Windsor (Canada), 2005
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