1,995 research outputs found
A novel coarse-to-fine remote sensing image retrieval system in JPEG-2000 compressed domain
Copyright 2018 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.This paper presents a novel content-based image search and retrieval (CBIR) system that achieves coarse to fine remote sensing (RS) image description and retrieval in JPEG 2000 compressed domain. The proposed system initially: i) decodes the code-streams associated to the coarse (i.e., the lowest) wavelet resolution, and ii) discards the most irrelevant images to the query image that are selected based on the similarities estimated among the coarse resolution features of the query image and those of the archive images. Then, the code-streams associated to the sub-sequent resolution of the remaining images in the archive are decoded and the most irrelevant images are selected by considering the features associated to both resolutions. This is achieved by estimating the similarities between the query image and remaining images by giving higher weights to the features associated to the finer resolution while assigning lower weights to those related to the coarse resolution. To this end, the pyramid match kernel similarity measure is exploited. These processes are iterated until the code-streams associated to the highest wavelet resolution are decoded only for a very small set of images. By this way, the proposed system exploits a multiresolution and hierarchical feature space and accomplish an adaptive RS CBIR with significantly reduced retrieval time. Experimental results obtained on an archive of aerial images confirm the effectiveness of the proposed system in terms of retrieval accuracy and time when compared to the standard CBIR systems
Content-based image retrieval of museum images
Content-based image retrieval (CBIR) is becoming more and more important with the advance of multimedia and imaging technology. Among many retrieval features associated with CBIR, texture retrieval is one of the most difficult. This is mainly because no satisfactory quantitative definition of texture exists at this time, and also because of the complex nature of the texture itself. Another difficult problem in CBIR is query by low-quality images, which means attempts to retrieve images using a poor quality image as a query. Not many content-based retrieval systems have addressed the problem of query by low-quality images. Wavelet analysis is a relatively new and promising tool for signal and image analysis. Its time-scale representation provides both spatial and frequency information, thus giving extra information compared to other image representation schemes. This research aims to address some of the problems of query by texture and query by low quality images by exploiting all the advantages that wavelet analysis has to offer, particularly in the context of museum image collections. A novel query by low-quality images algorithm is presented as a solution to the problem of poor retrieval performance using conventional methods. In the query by texture problem, this thesis provides a comprehensive evaluation on wavelet-based texture method as well as comparison with other techniques. A novel automatic texture segmentation algorithm and an improved block oriented decomposition is proposed for use in query by texture. Finally all the proposed techniques are integrated in a content-based image retrieval application for museum image collections
A Novel Active Contour Model for Texture Segmentation
Texture is intuitively defined as a repeated arrangement of a basic pattern
or object in an image. There is no mathematical definition of a texture though.
The human visual system is able to identify and segment different textures in a
given image. Automating this task for a computer is far from trivial. There are
three major components of any texture segmentation algorithm: (a) The features
used to represent a texture, (b) the metric induced on this representation
space and (c) the clustering algorithm that runs over these features in order
to segment a given image into different textures. In this paper, we propose an
active contour based novel unsupervised algorithm for texture segmentation. We
use intensity covariance matrices of regions as the defining feature of
textures and find regions that have the most inter-region dissimilar covariance
matrices using active contours. Since covariance matrices are symmetric
positive definite, we use geodesic distance defined on the manifold of
symmetric positive definite matrices PD(n) as a measure of dissimlarity between
such matrices. We demonstrate performance of our algorithm on both artificial
and real texture images
An Integrated Content and Metadata based Retrieval System for Art
In this paper we describe aspects of the Artiste project to develop a distributed content and metadata based analysis, retrieval and navigation system for a number of major European Museums. In particular, after a brief overview of the complete system, we describe the design and evaluation of some of the image analysis algorithms developed to meet the specific requirements of the users from the museums. These include a method for retrievals based on sub images, retrievals based on very low quality images and retrieval using craquelure type
Prototypicality effects in global semantic description of objects
In this paper, we introduce a novel approach for semantic description of
object features based on the prototypicality effects of the Prototype Theory.
Our prototype-based description model encodes and stores the semantic meaning
of an object, while describing its features using the semantic prototype
computed by CNN-classifications models. Our method uses semantic prototypes to
create discriminative descriptor signatures that describe an object
highlighting its most distinctive features within the category. Our experiments
show that: i) our descriptor preserves the semantic information used by the
CNN-models in classification tasks; ii) our distance metric can be used as the
object's typicality score; iii) our descriptor signatures are semantically
interpretable and enables the simulation of the prototypical organization of
objects within a category.Comment: Paper accepted in IEEE Winter Conference on Applications of Computer
Vision 2019 (WACV2019). Content: 10 pages (8 + 2 reference) with 7 figure
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