some-code-in-image-retrieval
所属分类：matlab编程
开发工具：matlab
文件大小：9282KB
下载次数：2
上传日期：2016-05-13 15:23:15
上 传 者：
mohammadfer
说明： some code for image retri that was written in matlab language
文件列表:
some code in image retrieval\1 (0, 2016-05-13)
some code in image retrieval\1\1208.6335.pdf (936037, 2015-08-15)
100 image dataset 2 without area and param.mat (1109884, 2015-05-05)
1000ImagesDataset.mat (1187701, 2015-05-05)
28.jpg (661933, 2015-08-07)
8.jpg (1077601, 2015-08-07)
9.jpg (743271, 2015-08-07)
blobFeatures.asv (2119, 2015-05-05)
cbires.fig (8484, 2015-05-05)
cbires.m (21029, 2015-05-05)
colorAutoCorrelogram.m (3108, 2015-05-05)
colorMoments.m (700, 2015-05-05)
confMatGet.m (1168, 2015-05-05)
confMatPlot.m (4684, 2015-05-05)
dataset_pca.mat (1324241, 2015-05-05)
dataset_with_img_names.mat (1232682, 2015-05-05)
dataset1.mat (13095, 2015-05-05)
dataset2.mat (118826, 2015-05-05)
dataset3.mat (125142, 2015-05-05)
dataset4.mat (37844, 2015-05-05)
dataset5.mat (35619, 2015-05-05)
dataset6.mat (22486, 2015-05-05)
dataset7.mat (23793, 2015-05-05)
dataset8.mat (3418, 2015-08-15)
extractShapeFeatures.m (2438, 2015-05-05)
gaborWavelet.m (19413, 2015-05-05)
greyLevelCoMatrix.m (245, 2015-05-05)
hsvHistogram.m (3437, 2015-05-05)
L1.m (1653, 2015-05-05)
L2.m (3099, 2015-05-05)
lowpassfilter.m (1640, 2015-05-05)
matPlot.m (4166, 2015-05-05)
output.csv (871310, 2015-05-05)
output.txt (2617, 2015-08-15)
reference.pdf (880347, 2015-05-05)
relativeDeviation.m (1643, 2015-05-05)
svm.m (4309, 2015-05-05)
waveletTransform.m (700, 2015-05-05)
... ...
The explosive growth of image data leads to the need of research and development of
Image retrieval. Image retrieval researches are moving from keyword, to low level features
and to semantic features. Drive towards semantic features is due to the problem of
the keywords which can be very subjective and time consuming while low level features
cannot always describe high level concepts in the users mind.
This Report is proposed a novel technique for objects spatial relationships semantics
extraction and representation among objects exists in images. All objects are identified
based on low level features extraction integrated with proposed line detection techniques.
This Report presents a framework for combining all the three i.e. color, texture
and shape information, and achieve higher retrieval efficiency. The novel method of
color histogram + color moment will be used to gives better results compared to others
using only single feature. More efficient data stuctures are used to store just non-zero
values of the GLCM in a linked list(Gray Level Co-occurrence Link List, GLCLL) or
an improved structure, linked list with hash table (Gray Level Co-occurrence Hybrid
Structure, GLCHS).
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