17,493 research outputs found
Human-Centered Computer Vision
Contains fulltext :
241512.pdf (Publisher’s version ) (Open Access)Symposium on The Art and Science of Pattern Recognitio
Radon-Gabor Barcodes for Medical Image Retrieval
In recent years, with the explosion of digital images on the Web,
content-based retrieval has emerged as a significant research area. Shapes,
textures, edges and segments may play a key role in describing the content of
an image. Radon and Gabor transforms are both powerful techniques that have
been widely studied to extract shape-texture-based information. The combined
Radon-Gabor features may be more robust against scale/rotation variations,
presence of noise, and illumination changes. The objective of this paper is to
harness the potentials of both Gabor and Radon transforms in order to introduce
expressive binary features, called barcodes, for image annotation/tagging
tasks. We propose two different techniques: Gabor-of-Radon-Image Barcodes
(GRIBCs), and Guided-Radon-of-Gabor Barcodes (GRGBCs). For validation, we
employ the IRMA x-ray dataset with 193 classes, containing 12,677 training
images and 1,733 test images. A total error score as low as 322 and 330 were
achieved for GRGBCs and GRIBCs, respectively. This corresponds to retrieval accuracy for the first hit.Comment: To appear in proceedings of the 23rd International Conference on
Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
Autoencoding the Retrieval Relevance of Medical Images
Content-based image retrieval (CBIR) of medical images is a crucial task that
can contribute to a more reliable diagnosis if applied to big data. Recent
advances in feature extraction and classification have enormously improved CBIR
results for digital images. However, considering the increasing accessibility
of big data in medical imaging, we are still in need of reducing both memory
requirements and computational expenses of image retrieval systems. This work
proposes to exclude the features of image blocks that exhibit a low encoding
error when learned by a autoencoder (). We examine the
histogram of autoendcoding errors of image blocks for each image class to
facilitate the decision which image regions, or roughly what percentage of an
image perhaps, shall be declared relevant for the retrieval task. This leads to
reduction of feature dimensionality and speeds up the retrieval process. To
validate the proposed scheme, we employ local binary patterns (LBP) and support
vector machines (SVM) which are both well-established approaches in CBIR
research community. As well, we use IRMA dataset with 14,410 x-ray images as
test data. The results show that the dimensionality of annotated feature
vectors can be reduced by up to 50% resulting in speedups greater than 27% at
expense of less than 1% decrease in the accuracy of retrieval when validating
the precision and recall of the top 20 hits.Comment: To appear in proceedings of The 5th International Conference on Image
Processing Theory, Tools and Applications (IPTA'15), Nov 10-13, 2015,
Orleans, Franc
Gabor Barcodes for Medical Image Retrieval
In recent years, advances in medical imaging have led to the emergence of
massive databases, containing images from a diverse range of modalities. This
has significantly heightened the need for automated annotation of the images on
one side, and fast and memory-efficient content-based image retrieval systems
on the other side. Binary descriptors have recently gained more attention as a
potential vehicle to achieve these goals. One of the recently introduced binary
descriptors for tagging of medical images are Radon barcodes (RBCs) that are
driven from Radon transform via local thresholding. Gabor transform is also a
powerful transform to extract texture-based information. Gabor features have
exhibited robustness against rotation, scale, and also photometric
disturbances, such as illumination changes and image noise in many
applications. This paper introduces Gabor Barcodes (GBCs), as a novel framework
for the image annotation. To find the most discriminative GBC for a given query
image, the effects of employing Gabor filters with different parameters, i.e.,
different sets of scales and orientations, are investigated, resulting in
different barcode lengths and retrieval performances. The proposed method has
been evaluated on the IRMA dataset with 193 classes comprising of 12,677 x-ray
images for indexing, and 1,733 x-rays images for testing. A total error score
as low as ( accuracy for the first hit) was achieved.Comment: To appear in proceedings of The 2016 IEEE International Conference on
Image Processing (ICIP 2016), Sep 25-28, 2016, Phoenix, Arizona, US
Image mining: issues, frameworks and techniques
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an
interdisciplinary endeavor that draws upon expertise in
computer vision, image processing, image retrieval, data
mining, machine learning, database, and artificial
intelligence. Despite the development of many
applications and algorithms in the individual research
fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper
Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval
Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance
- …