29-12-2012, 06:57 PM
Content-Based Image Retrieval System Based on Self Organizing Map, Fuzzy Color Histogram and Subtractive Fuzzy Clustering
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Introduction
In Content-Based Image Retrieval (CBIR), researchers
seek for efficient and robust methods to retrieve
relevant images from huge images database utilizing
automatic derivation of local and global features from
image query as well as images database. Features as
shape, color, and texture are the most dominant
features to be considered. There are many similarity or
dissimilarity measures to rank the retrieved images
based on its relevancy to the query image.
Previous Work
In [2], they propose probabilistic framework to process
multiple image queries. The proposed framework is
independent from similarity measures and gives rise to
a relevance feedback mechanism. In [26], CBIR
method to diagnose aid in medical images is proposed.
Images are indexed without extracting domain-specific
features; a signature is built for each image via wavelet
transform. In [10], they propose two CBIR frameworks
based on genetic programming. The first framework is
concerned with user indication of relevant images,
while the second one considers the relevant and nonrelevant
indicated images. In [27], new multiresolution
fusion Algorithm for spatially registered
multi-sensor fusion is proposed.
SOM Indexing
SOM (Self-Organizing Map) [17] is used as an
indexing technique to organize the feature vectors due
to its efficiency in organizing unsupervised statistical
data.
A SOM consists of a regular grid of map units. A
model vector is d
mv ∈ℜ associated with each map unit
v. The map tries to represent all available observation
d
x ∈ ℜ with optimal accuracy. The fitting of the model
vectors is a sequential regression process, where t=0,
1, 2,…, tmax − 1 is the step index.
Edge Detection Enhancement
Many of edge detectors are available to researchers
[19]. Marr and Hildreth convolve a mask over the
image and label zero-crossings of the convolution
output as edge points [20]. In [12], an approach
combining contrast threshold and analysis of direction
dispersion to find edges is presented. In [3], they label
peaks in the magnitude of the first derivative of the
intensity profile along a scan-line as feature points for
matching. Other popular gradient edge detectors are
the Canny, Roberts, Sobel and Prewitt operators [4].
Comparing objects based on edge operators only does
not yields to satisfactory results in most cases. That
because if there is any variation in image brightness,
then the same image looks different after applying the
edge operator. Moreover, the unwanted pixels in the
image affect the retrieval accuracy dramatically. In this
research and in order to overcome some of these
problems an Algorithm to filter the images at the preprocessing
stage is proposed.