Mixture of Visual Features For Content-Based Image Retrieval

Aarti S. Datir, Dipak V. Patil


Image retrieval system is a computer system for
browsing, searching and retrieve images from a huge data set
of digital images. Content-based image retrieval (CBIR) is, the
problem of searching for digital images in large datasets. CBIR
retrieves similar images from huge image dataset based on image
features. Content-based means that the searching will evaluate
the actual contents of the image. The content of image might
refer colors, shapes, textures, or any other information that can
be derived from the image itself. There are two major approaches
to content-based image retrieval using local image descriptors.
One of them is descriptor by descriptor matching and the another
one is based on comparison of global image representation that
describes the set of local descriptors of each image. Image
representation is one of the key issues for large-scale CBIR.
Using MPEG-7 descriptors and local descriptors more number of
features are extracted from given query image and to reduce the
feature size principle component analysis(PCA) is used. These
features are embedded and aggregated into a compact vector to
avoid indexing each feature individually. In the embedding step,
each local descriptor is mapped into a high dimensional vector.
The aggregation step integrates all the embedded vectors of an
image into a single vector which obtains a compact representation
for image retrieval. Subsequently, k-means algorithm is used,
clusters are formed and images are trained in addition cosine
similarity measure is used to increase the retrieval performance.
In re-ranking step, Euclidean distance is used to find similarity
and provide efficient searching.

Full Text:



ZhanningGao, JianruXue, Wengang Zhou, Shanmin Pang, and Qi Tian,

”Democratic Diffusion Aggregation for Image Retrieval” , IEEE Trans.

Multimedia, , vol. 18, no. 8, pp. 16611674, Aug. 2016

Nidhi Singhai et al,”A Survey On: Content Based Image Retrieval

Systems”, International Journal of Computer Applications (0975 8887)

Volume 4 No.2, July 2010

Monika Jain and Dr. S.K.Singh,”A Survey On: Content Based Image

Retrieval Systems Using Clustering Techniques For Large Data sets”,

International Journal of Managing Information Technology (IJMIT) Vol.3,

No.4, November 2011

Aarti Datir and D. V. Patil,” Survey on Different Techniques of Content

Based Image Retrieval”, International Journal of Science Technology

Management and Research, Volume 1, Issue 8, November 2016

P.S. Malge and Pasnur M.A. ,” Performance Evaluation of Texture based

Image retrieval”, International Journal of Computer Applications (0975

Volume 72 No.2, May 2013

Hamid A. Jalab,”Image Retrieval System Based on Color Layout Descriptor and Gabor Filters”,IEEE conf. on open systems,sep 2011

Dong Kwon Park et al. Efficient Use of Local Edge Histogram Descriptor.

Rafael do Esprito Santo,”Principal Component Analysis applied to digital

image compression”,Study carried out at Institute of Cerebro,jun 2012.

R. Arandjelovic and A. Zisserman,”Three things everyone should know

to improve object retrieval,” in Proc. IEEE Conf. Comput. Vis. Pattern

Recog., Jun. 2012, pp. 29112918.

R. Arandjelovic and A. Zisserman,”All about VLAD,” in Proc. IEEE

Conf. Comput. Vis. Pattern Recog., Jun. 2013, pp. 15781585

A. Babenko and V. Lempitsky,”The inverted multi-index,” in Proc. IEEE

Conf. Comput. Vis. Pattern Recog., Jun. 2012, pp. 30693076

L. Bo and C. Sminchisescu,”Efficient match kernel between sets of

features for visual recognition,” in Proc. Adv. Neural Inf. Process. Syst.,

, pp. 135143

Y.-L. Boureau, J. Ponce, and Y. LeCun,”A theoretical analysis of feature

pooling in visual recognition,” in Proc. Int. Conf. Mach. Learn., 2010,


L. Chu, S. Jiang, S. Wang, Y. Zhang, and Q. Huang,”Robust spatial

consistency graph model for partial duplicate image retrieval,” IEEE

Trans. Multimedia, vol. 15, no. 8, pp. 19821996, Jun. 2013

O. Chum and J. Matas,”Unsupervised discovery of co-occurrence in

sparse high dimensional data,” in Proc. IEEE Conf. Comput. Vis. Pattern

Recog., Jun. 2010, pp. 34163423

O. Chum, J. Philbin, J. Sivic, M. Isard, and A. Zisserman,”Total recall:

Automatic query expansion with a generative feature model for object

retrieval,” in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2007, pp. 18. 91

H. Jegou, M. Douze, and C. Schmid,”On the burstiness of visual

elements,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2009,

pp. 11691176

H. Jegouet al.,”Aggregating local image descriptors into compact codes,”

IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 9, pp. 17041716,

Sep. 2012

H. Jegou and A. Zisserman,”Triangulation embedding and democratic

aggregation for image search”, in Proc. IEEE Conf. Comput. Vis. Pattern

Recog., Jun. 2014, pp. 33103317

H. Jegou and O. Chum,”Negative evidences and co-occurences in image

retrieval: The benefit of PCA and whitening,” in Proc. Eur. Conf. Comput.

Vis., 2012, pp. 774787

J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman,”Lost in

quantization: Improving particular object retrieval in large scale image

databases,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog., Jun. 2008,

pp. 18

G. Tolias and H. Jegou,”Visual query expansion with or without geometry: Refining local descriptors by feature aggregation,” Pattern Recog.,

vol. 47, no. 10, pp. 34663476, 2014

R. G. Cinbis, J. Verbeek, and C. Schmid,”Image categorization using

fisher kernels of non-IID image models,” in Proc. IEEE Conf. Comput.

Vis. Pattern Recog., Jun. 2012, pp. 21842191

Takashi Takahashi, Takio Kurita,” Mixture of Subspaces Image Representation and Compact Coding for Large-Scale Image Retrieval”, IEEE

Trans. on Pattern Anal. and Mach. Intell. , JULY 2015, VOL. 37, NO.

,PP .1469-1479

Mohammed Alkhawlani, Mohammed Elmogy, HazemElbakry, ”ContentBased Image Retrieval using Local Features Descriptors and Bag-ofVisual Words, (IJACSA) International Journal of Advanced Computer

Science and Applications, Vol. 6, No. 9, 2015

Sukhdeep kaur, Deepak Aggarwal, ”Image Content based retrieval system using cosine simi


  • There are currently no refbacks.


Copyright © IJETT, International Journal on Emerging Trends in Technology