Selecting the Best Sub-Region Indexing the Images in the Case of Weak Segmentation Based On Local Color Histograms
Color Histogram is considered as the oldest method
used by CBIR systems for indexing images. In turn, the global
histograms do not include the spatial information; this is why the
other techniques coming later have attempted to encounter this
limitation by involving the segmentation task as a preprocessing step.
The weak segmentation is employed by the local histograms while
other methods as CCV (Color Coherent Vector) are based on strong
segmentation. The indexation based on local histograms consists of
splitting the image into N overlapping blocks or sub-regions, and
then the histogram of each block is computed. The dissimilarity
between two images is reduced, as consequence, to compute the
distance between the N local histograms of the both images resulting
then in N*N values; generally, the lowest value is taken into account
to rank images, that means that the lowest value is that which helps to
designate which sub-region utilized to index images of the collection
being asked. In this paper, we make under light the local histogram
indexation method in the hope to compare the results obtained against
those given by the global histogram. We address also another
noteworthy issue when Relying on local histograms namely which
value, among N*N values, to trust on when comparing images, in
other words, which sub-region among the N*N sub-regions on which
we base to index images. Based on the results achieved here, it seems
that relying on the local histograms, which needs to pose an extra
overhead on the system by involving another preprocessing step
naming segmentation, does not necessary mean that it produces better
results. In addition to that, we have proposed here some ideas to
select the local histogram on which we rely on to encode the image
rather than relying on the local histogram having lowest distance with
the query histograms.
 Shapiro, L .et Haralick, R. Glossary of computer vision terms. Pattern
Recognition. 1991. Vol. 24, 1, pp. 69-93.
 Qiu, G. P. (2004). Embedded colour image coding for content-based
retrieval. Journal of Visual Communication and Image Representation,
 Statistical and structural approaches to texture. Haralick, R. 1979.
Proceedings of IEEE. Vol. 67, pp. 786-804.
 Huang, P. W., & Dai, S. K. (2006). Texture image retrieval and image
segmentation using composite sub-band gradient vectors. Journal of
Visual Communication and Image Representation, 17(5), 947–957.
 Ko, B. C., &Byun, H. (2005). FRIP: A region-based image retrieval tool
using automatic image segmentation and stepwise boolean and
matching. IEEE Transactions on Multimedia, 7(1), 105–113.
 Long, Fuhui, Zhang, Hongjianget Feng, David D. Multimedia
Information Retrieval and Management - Technological Fundamentals
and Applications.s.l.: Springer, 2002.
 Swain M. J., Ballard D. H. (1991), « Color Indexing ». International
Journal of Computer Vision, Vol. 7, no. 1, pp. 11-22, 1991.
 Neetu Sharma. S, PareshRawat. S and Jaikaran Singh.S "Efficient CBIR
Using Color Histogram Processing”. Signal & Image Processing : An
International Journal (SIPIJ) Vol. 2, No. 1, March 2011.
 Gong Y, Chuan C.H, Xiaoyi G. Image indexing and and retrieval using
color histograms, Multimedia Tools and Applications, vol.2 pp. 133-
 Guojun Lu. Multimedia database management systems, chapter 6, pp
131-177,Artech House, 1999.
 Babu, G. P., B. M. Mehre and M. S. Kankanhalli, 1995. Color indexing
for efficient image retrieval. Multimedia Tools Appli., 1: 327-348. DOI: