cbir for sketches in image processing-1002
SVD Based Features for Image Retrieval
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NST Sai, RC Patil ,International Journal of Computer Science and Artificial ,jcsai.org
The term CBIR [10-25] seems to have originated in 1992, when it was used by T of retrieving
desired images from a large collection on the basis of syntactical image features that are used
originate from fields such as statistics, pattern recognition, signal processing, and computer
Sinimbu-Multimodal queries to support biodiversity studies
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Keywords: Biodiversity data management, CBIR, Ontologies include images (in particular, photos
of animals or plants), sound recordings (of animals), sketches, etc. standard text-based queries
with ontology manipulation (for semantic enhancement) and query by image content
Multilayer Neural Networks and Nearest Neighbor Classifier Performances for ImageAnnotation
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M OUJAOURA, B MINAOUI, M FAKIR ,annotation, 2012
It is very important in many applications for any image processing, and it still remains
a challenge for scientists and researchers. So far, the efforts and attempts are still
being made to improve the segmentation techniques. With
AN EFFICIENT CONTENT BASED IMAGE RETRIEVAL METHOD FOR RETRIEVING IMAGES
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QN Huu, HNT Thu, TN Quoc ,ijicic.org
of Soc. Photo. Opt. Instrum. Eng., USA, pp.173-187, 1993. [12] S. Wang, A robust CBIR approach
using local color histogram, Technique Report TR 01-13, 2001. [14] RC Gonzalez and RE Woods,
Digital Image Processing, Addison-Wesley, New York, 2000. Page 13.
Image Mining in the Context of Content Based Image Retrieval: A Perspective
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N Mishra, S Silakari, RGP Vishwavidyalaya ,2012
The similarities /distances between the feature vectors of the query example or sketch and those
of the images in the The indexing scheme provides an efficient way to search for the image
database [9]. All the mathematical expressions defined in 3 Generalized CBIR Systems
Scene Image Classification and Retrieval
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W Liu ,2012 ,dspace.cc.tut.fi
Averaged Normalized Modified Retrieval Rate ARP Angular Radial Partitioning BoW Bag of Words
CBIR Content Based NCA Neighbourhood Components Analysis PCA Principal Component
Analysis RBF Radial Basis Function SBIR Sketch Based Image Retrieval SE
Trace transform based method for color image domain identification
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THE importance of context is very well known in Content Based Image Retrieval (CBIR) [1], [2].
Many low-level features, based on shape, texture, color and other local descriptors broadly used
in computer vision incur under In Section 2.1, image pre-processing issues are
Using Color Sequences for Cartoon Image Retrieval
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CH Su, CH Lin, HS Huang, KC Fan ,Advanced Materials Research, 2012 ,Trans Tech Publ
For comprehensive study of previous CBIR techniques, please see [2~5]. Some current successful
cartoon images retrieval systems have been reported in the [7]Xiangbin Zhu , “Cartoon Image
Classification Based [8]Yejun Wu, “Searching Digital Political Cartoons”, 2010 IEEE
CONTENT BASED IMAGE RETRIEVAL FOR MEDICAL IMAGES
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YF Fathabad, MA Balafar ,iotpe.com
QUERY FORMULATION BY IMAGE CONTENT Most systems in CBIR use the query by
example(s) (QBE) paradigm which needs an appropriate starting image for querying feature query •
Feature combination query • Localized feature query • Query by sketch • User-dawned
An audio-visual approach to web video categorization
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using MPEG coefficients [2]. Color descriptors are generally derived at the image level and in
distinguishing between seven video genres and sub-genres, namely football, cartoons, music,
weather 1 Processing a time (OX axis)—frequency (OY axis) representation in terms of
An Image Retrieval System Based on the Color Complexity of Images
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YK Chan, YT Liu, RC Chen ,Computing and Informatics, 2012 ,cai.sk
Various approaches have been proposed to extract different types of image features to sup- port
CBIR. The CVPWIS sketches the color variations of the pixels within Page 12. 506 YK Chan, YT
Liu, RC Chen IEEE transactions on Image Processing, Vol. 11, 2002, No. 8, pp.
Multimedia search and retrieval using multimodal annotation propagation and indexing techniques
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will be able to handle specific types of multimedia (text, 2D image, sketch, video, 3D Machines
(SVMs), namely the Multiple Instance Learning (MIL)-based (applied to image blocks) and
suggested the use of inverted index for Content-Based Information Retrieval (CBIR) is the
Mistura de cores: uma nova abordagem para processamento de cores e sua aplicação na segmentação de imagens
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O Severino Junior ,2012 ,teses.usp.br
paint to create oil paintings, and also on observations of the distribution of cones in human retina
for the interpretation of these colors, this thesis proposes an image processing technique based
The results show the performance of the face sketch image in CBIR applications.
Image Annotation using Moments and Multilayer Neural Networks
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M Oujaoura, B Minaoui, M Fakir ,International Journal
It plays a very important role in many applications for image processing. Legendre moments. •
Zernike moments. 4.1 Hu moments Hu moments are widely used in the image processing and
pattern recognition. They are derived and calculated from geometric moments.
Hierarchical clustering relevance feedback for content-based image retrieval
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. Boucher, Text Retrieval Relevance Feedback Techniques for Bag- of-Words Model in CBIR,
International Conference M. Mehrotra, S. Beck- man, Relevance feedback: a power tool for
interactive content-based image retrieval, IEEE 6] S. Liang, Z. Sun, Sketch retrieval and
Iconic Search: Visual Image Retrieval by Sample Selection
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N Bouhendi ,2012 ,dspace.mah.se
For instance, a user might use both keywords and sketches to define his requirements.
The examples of IR’s that use each method will be described later in section ?4. Page
14. Image Retrievals and Terminology 6 2.4. Query Processing
Visual Image Search: Feature Signatures or/and Global Descriptors
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thus in a very efficient search mechanism, – and introduce a new image collection for CBIR
effectiveness evaluation Given an example multimedia object or sketch q, the multimedia database
S ? U (where U is Visual Image Search: Feature Signatures or/and Global Descriptors
A Technique to Content-Based Video Retrieval Utilizing Diverse Set of Features
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S Padmakala ,European , 2012 ,europeanjournalofscientificresearch.
seem to be an inherent extension (or combination) of Content-based Image Retrieval (CBIR)
systems [31 color and motion extracted from the query sketch by a Viterbi process more visual
materials are created and stored in visual information systems (VIS) ie, image databases or
Development of shape descriptors based on legendre polynomials and content based retrieval of scoliosis images
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VP Dinesh Kumar ,2012 ,shodhganga.inflibnet.ac.in
Thus this descriptor is suitable for tracking shapes in video data processing applications. sought
is a difficult task. In CBIR systems, image is represented using a high dimensional feature vector
and uses feature dependent similarity measures, which are not simple to tune
Shape-Based Invariant Feature Extraction for Object Recognition
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are pro- posed based on visual features derived directly from the content of the image: these
are the content-based image retrieval (CBIR) approaches. They allow users to search the desired
image by specifying image queries: a query can be an exam- ple, a sketch or visual
Searching through photographic databases with QuickLook
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One of the first attempts to integrate and compare semantic keyword and low-level features into
a single CBIR framework is the the user to search an image data base with the aid of sample
images, or a user- made sketch, and/or on Image Processing 10(1), 117–130 (2001).
Automated Metadata Extraction from Art Images
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Using an example image is perhaps the most popular way of querying a CBIR system in the
absence of reliable metadata; Graphics, or query by sketch: This consists of a hand-drawn
or computer-generated picture, where graphics are used as a query.
Recognition and retrieval of mathematical expressions
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R Zanibbi, D Blostein ,International Journal on Document Analysis and , 2012 ,Springer
The input to a math recognition system can take three forms: vector graphics (such as PDF),
strokes (such as pen strokes on a data tablet), or adocument image. The processing that is needed
to extract expressions and recognize charac- ters depends greatly on the form of input
Contributions to hyperspectral image processing from Lattice Computing and Computational Intelligence
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MAV Bodón ,2012
Page 5. Contributions to hyperspectral image processing from Lattice Computing and
Computational Intelligence by This Thesis has grown along two main lines of work. First, the idea
of developing Content Based Image Retrieval (CBIR) systems for hyper- spectral images.
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