6,728 research outputs found
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching
This paper proposes a content based image retrieval (CBIR) system using the
local colour and texture features of selected image sub-blocks and global
colour and shape features of the image. The image sub-blocks are roughly
identified by segmenting the image into partitions of different configuration,
finding the edge density in each partition using edge thresholding followed by
morphological dilation. The colour and texture features of the identified
regions are computed from the histograms of the quantized HSV colour space and
Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture
feature vectors is computed for each region. The shape features are computed
from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching
(IRM) algorithm is used for finding the minimum distance between the sub-blocks
of the query and target image. Experimental results show that the proposed
method provides better retrieving result than retrieval using some of the
existing methods.Comment: 7 page
The aceToolbox: low-level audiovisual feature extraction for retrieval and classification
In this paper we present an overview of a software platform
that has been developed within the aceMedia project,
termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 eXperimental Model (XM),
with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images
Content-based Image Retrieval by Information Theoretic Measure
Content-based image retrieval focuses on intuitive and efficient methods for retrieving images from databases based on the content of the images. A new entropy function that serves as a measure of information content in an image termed as 'an information theoretic measure' is devised in this paper. Among the various query paradigms, 'query by example' (QBE) is adopted to set a query image for retrieval from a large image database. In this paper, colour and texture features are extracted using the new entropy function and the dominant colour is considered as a visual feature for a particular set of images. Thus colour and texture features constitute the two-dimensional feature vector for indexing the images. The low dimensionality of the feature vector speeds up the atomic query. Indices in a large database system help retrieve the images relevant to the query image without looking at every image in the database. The entropy values of colour and texture and the dominant colour are considered for measuring the similarity. The utility of the proposed image retrieval system based on the information theoretic measures is demonstrated on a benchmark dataset.Defence Science Journal, 2011, 61(5), pp.415-430, DOI:http://dx.doi.org/10.14429/dsj.61.117
Color Name Applications in Computer Vision
In Computer Vision, the association of names to colors is one of the fundamental problems in the field of image understanding. There are numerous computational applications (e.g. image retrieval, visual tracking, person identification, human-machine interaction, etc.) that require pixels to be labelled according to the color perceived by the user. This is relatively easy for focal colors under canonical illuminants, where the agreement is high, but becomes increasingly difficult as perceptions move away from these conditions. For these difficult cases, the traditional solution tends to be a collection of "ad-hoc" strategies, however, new approaches that combine knowledge from anthropology, linguistics, visual perception and machine learning have offered promising results. Specifically, deep neural networks appear to possess all the required building blocks to offer a color naming solution "in the wild". This article reviews the current state of knowledge and discusses open challenges with a multidisciplinary (and non-specialized) readership in mind
An analysis of the use of graphics for information retrieval
Several research groups have addressed the problem of retrieving vector graphics. This work has, however, focused either on domain-dependent areas or was based on very simple graphics languages. Here we take a fresh look at the issue of graphics retrieval in general and in particular at the tasks which retrieval systems must support. The paper presents a series of case studies which explored the needs of professionals in the hope that these needs can help direct future graphics IR research. Suggested modelling techniques for some of the graphic collections are also presented
Medical Image Classification via SVM using LBP Features from Saliency-Based Folded Data
Good results on image classification and retrieval using support vector
machines (SVM) with local binary patterns (LBPs) as features have been
extensively reported in the literature where an entire image is retrieved or
classified. In contrast, in medical imaging, not all parts of the image may be
equally significant or relevant to the image retrieval application at hand. For
instance, in lung x-ray image, the lung region may contain a tumour, hence
being highly significant whereas the surrounding area does not contain
significant information from medical diagnosis perspective. In this paper, we
propose to detect salient regions of images during training and fold the data
to reduce the effect of irrelevant regions. As a result, smaller image areas
will be used for LBP features calculation and consequently classification by
SVM. We use IRMA 2009 dataset with 14,410 x-ray images to verify the
performance of the proposed approach. The results demonstrate the benefits of
saliency-based folding approach that delivers comparable classification
accuracies with state-of-the-art but exhibits lower computational cost and
storage requirements, factors highly important for big data analytics.Comment: To appear in proceedings of The 14th International Conference on
Machine Learning and Applications (IEEE ICMLA 2015), Miami, Florida, USA,
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