Download - Automatic Image Annotation
Automatic Image Annotation and Retrieval Using the Joint
Composite Descriptor
Konstantinos Zagoris, Savvas A. Chatzichristofis, Nikos Papamarkos and Yiannis S. Boutalis
Department of Electrical & Computer EngineeringDemocritus University of Thrace, Xanthi, Greece
Problem Definition Today, capable tools are needed in order to
successfully search and retrieve images. Content-based Image Retrieval Techniques
have been used such as img(Anaktisi) and img(Rummager) which employ low-level image features such as color, texture and shape in order to locate similar images.
Although the above approaches are successful, they lack the ability to include human perception in the query
Proposed Technique A new image annotation technique A keyword-based image retrieval system Employ the Joint Composite Descriptor Utilizes two set of keywords One Set consists of colors – keywords The other Set consists of words The queries can be more naturally
specified by the user
The Keyword Sets
Keyword Annotation Image Retrieval SystemThe Overall Structure of the Proposed System
Joint Composite Descriptor Belong to the family of Compact
Composite Descriptors More than one feature at the same time,
in a very compact representation Global Descriptor (global image low
level features) Fusion of the Color and Edge Directivity
Descriptor (CEDD) and the Fuzzy Color and Texture Histogram (FCTH)
CEDD and FCTH Descriptors The CEDD length is 54 bytes per image while
FCTH length is 72 bytes per image. The structure of these descriptors consists of
n texture areas. In particular, each texture area is separated into 24 sub regions, with each sub region describing a color.
CEDD and FCTH use the same color information, as it results from 2 fuzzy systems that map the colors of the image in a 24-color custom pallete.
CEDD and FCTH Descriptors
CEDD FCTH
Color Information given by the two descriptors comes from the same fuzzy system.
CEDD uses a fuzzy version of the five digital filters proposed by the MPEG-7 Edge Histogram Descriptor.
FCTH uses the high frequency bands of the Haar wavelet Transform in a fuzzy system.
So, the joining of the descriptors is accomplished by the fusion-combination of the texture areas carried by each descriptor.
CEDD and FCTH Descriptors
Each descriptor can be descripted in the following way:
Joint Composite Descriptor (JCD)
with
Joint Composite Descriptor (JCD)
Color Similarity Grade (CSG) Defines the amount of corresponding
color in the image It is calculated from the JCD for each
color keyword
Color Similarity Grade (CSG)
Portrait Similarity Grade (PSG) Defines the connection of the image
depiction with the corresponding word It is calculated from the normalization of
a trained Support Vector Machines (SVM) Decision Function
For each word a SVM is trained using as training samples the JCD values from a small subset of the available image database
Support Vector Machines (SVMs) Based on statistical learning theory Separate the space that the training samples are
resided in two classes The new sample is classified depending where in
the space residues
Portrait Similarity Grade (PSG) In this work, we used the following
equation to determine the membership value of the sample x to the class 1:
1 1100 max , 0
1 11 13 3
1 1100 1 max , 0
1 11 13 3
f x f x
f x f x
if f xe e
R x
if f xe e
Keyword Annotation Image Retrieval System User can employ a number of keywords from both sets
in order to describe the image. For each keyword , and initial classification position is
calculated based on Manhattan distance and its PSG or its CSG.
Then the final Rank is calculated for each image based on:
where N is the total number of images in the database For more keywords the sum of the Ranks for each
keyword is calculated:+
ImplementationThe proposed work is implemented as part of the img(Anaktisi) web application at http://www.anaktisi.net
Evaluation The Keyword based image retrieval is
tested in the Wang and the NISTER databases
Image Retrieval Results using the keyword “mountain”
Image retrieval results using the JCD of the first image as query.
Conclusions A automatic image annotation method which
maps the low level features to a high level features which humans employ.
This method provides two distinct sets of keywords: colors and words.
The proposed project has been implemented and evaluated on the Wang and NISTER databases.
The results demonstrate the method's effectiveness as it retrieved results more consistent with human perception.
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