20 research outputs found
Color Texture Classification Approach Based on Combination of Primitive Pattern Units and Statistical Features
Texture classification became one of the problems which has been paid much
attention on by image processing scientists since late 80s. Consequently, since
now many different methods have been proposed to solve this problem. In most of
these methods the researchers attempted to describe and discriminate textures
based on linear and non-linear patterns. The linear and non-linear patterns on
any window are based on formation of Grain Components in a particular order.
Grain component is a primitive unit of morphology that most meaningful
information often appears in the form of occurrence of that. The approach which
is proposed in this paper could analyze the texture based on its grain
components and then by making grain components histogram and extracting
statistical features from that would classify the textures. Finally, to
increase the accuracy of classification, proposed approach is expanded to color
images to utilize the ability of approach in analyzing each RGB channels,
individually. Although, this approach is a general one and it could be used in
different applications, the method has been tested on the stone texture and the
results can prove the quality of approach.Comment: The International Journal of Multimedia & Its Applications (IJMA)
Vol.3, No.3, August 201
Dispelling Classes Gradually to Improve Quality of Feature Reduction Approaches
Feature reduction is an important concept which is used for reducing
dimensions to decrease the computation complexity and time of classification.
Since now many approaches have been proposed for solving this problem, but
almost all of them just presented a fix output for each input dataset that some
of them aren't satisfied cases for classification. In this we proposed an
approach as processing input dataset to increase accuracy rate of each feature
extraction methods. First of all, a new concept called dispelling classes
gradually (DCG) is proposed to increase separability of classes based on their
labels. Next, this method is used to process input dataset of the feature
reduction approaches to decrease the misclassification error rate of their
outputs more than when output is achieved without any processing. In addition
our method has a good quality to collate with noise based on adapting dataset
with feature reduction approaches. In the result part, two conditions (With
process and without that) are compared to support our idea by using some of UCI
datasets.Comment: 11 Pages, 5 Figure, 7 Tables; Advanced Computing: An International
Journal (ACIJ), Vol.3, No.3, May 201
An Innovative Skin Detection Approach Using Color Based Image Retrieval Technique
From The late 90th, "Skin Detection" becomes one of the major problems in
image processing. If "Skin Detection" will be done in high accuracy, it can be
used in many cases as face recognition, Human Tracking and etc. Until now so
many methods were presented for solving this problem. In most of these methods,
color space was used to extract feature vector for classifying pixels, but the
most of them have not good accuracy in detecting types of skin. The proposed
approach in this paper is based on "Color based image retrieval" (CBIR)
technique. In this method, first by means of CBIR method and image tiling and
considering the relation between pixel and its neighbors, a feature vector
would be defined and then with using a training step, detecting the skin in the
test stage. The result shows that the presenting approach, in addition to its
high accuracy in detecting type of skin, has no sensitivity to illumination
intensity and moving face orientation.Comment: 9 Pages, 4 Figure