Object-Based Image Indexing and Retrieval in DCT Domain using Clustering Techniques
In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.
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of Computer Vision, 1991, vol.7, no.1, pp.11-32.
[2] H.Nezamabadi-pour, and E.Kabir,"Image retrieval using histograms
of unicolor and bicolor blocks and directional changes in intensity
gradient", 2004, Pattern Recognition Letters, vol. 25, no.14, pp. 1547-
1557.
[3] F.Mokhtarian and S.Abbasi, "Shape similarity retrieval under affine
transforms", Pattern Recognition, 2002, vol. 35, pp. 31-41.
[4] A.K.Jain and A.Vailaya, "Image retrieval using color and shape",
Pattern Recognition, 1996, vol.29, no.8, pp.1233-1244.
[5] B.S.Manjunath and W.Y.Ma, "Texture feature for browsing and
retrieval of image data", IEEE PAMI, 1996, no. 18, vol. 8, pp. 837-
842.
[6] J.R.Smith and C.S.Li, "Image classification and quering using
composite region templates", Academic Press, Computer Vision and
Understanding, 1999, vol.75, pp.165-174.
[7] C.W.Ngo, T.C.Pong and R.T.Chin, "Exploiting image indexing
techniques in DCT domain", pattern Recognition, 2001, vol. 34, pp.
1841-1851.
[8] J.Jiang, A.Armstrong and G.C.Feng, "Direct content access and
extraction from JPEG compressed images", Pattern Recognition,
2002, vol. 35, pp. 2511-2519.
[9] G.Feng and J.Jiang, "JPEG compressed image retrieval via statistical
features", Pattern Recognition, 2003, vol. 36, pp. 977-985.
[10] S.Climer and S.K.Bhatia, "Image database indexing using JPEG
coefficients", Pattern Recognition, 2002, vol. 35, pp. 2479-2488.
[11] P.Ladret and A.G.Dugue, "Categorization and retrieval of scene
photographs from a JPEG compressed database", Pattern Analysis
and Applications, 2001, no. 4, pp. 185-199.
[12] J.Z.Wang, J.Li and G.Wiederhold, "SIMPLIcity: semantic sensitive
integrated matching for picture libraries", IEEE Trans. on Pattern
Analysis and Machine Intelligence, 2001, vol.23, no.9, pp.947-963.
[13] H.W.Yoo, S.H.Jung, D.H.Jang and Y.K.Na, "Extraction of major
object features using VQ clustering for content-based image
retrieval", Pattern Recognition, 2002, vol. 35, pp. 1115-1126.
[14] W.Pennebaker and J.Mitchell, "JPEG still image data compression
standard" , 1993, New York: Vann strand.
[15] A.Vailaya, A.K.Jain and H.J.Zhang, "On image classification: city vs.
landscape", Pattern Recognition, 1998, vol. 31, pp. 1921-1935.
[16] M.Szummer and R.W.Picard, "Indoor-outdoor image classification",
IEEE International Workshop on Content-Based Access of Image and
Video Database, in conj. With ICCV-98, Bombay, 1998.
[17] Y.Rubner, J.Puzicha, C.Tomasi and J.M.Buhmann, "Empirical
evaluation of dissimilarity measures for color and texture", Computer
Vision and Image Understanding, 2001, vol. 84, pp. 25-43.
[1] M.J.Swain and D.H.Ballard, "Color indexing", International Journal
of Computer Vision, 1991, vol.7, no.1, pp.11-32.
[2] H.Nezamabadi-pour, and E.Kabir,"Image retrieval using histograms
of unicolor and bicolor blocks and directional changes in intensity
gradient", 2004, Pattern Recognition Letters, vol. 25, no.14, pp. 1547-
1557.
[3] F.Mokhtarian and S.Abbasi, "Shape similarity retrieval under affine
transforms", Pattern Recognition, 2002, vol. 35, pp. 31-41.
[4] A.K.Jain and A.Vailaya, "Image retrieval using color and shape",
Pattern Recognition, 1996, vol.29, no.8, pp.1233-1244.
[5] B.S.Manjunath and W.Y.Ma, "Texture feature for browsing and
retrieval of image data", IEEE PAMI, 1996, no. 18, vol. 8, pp. 837-
842.
[6] J.R.Smith and C.S.Li, "Image classification and quering using
composite region templates", Academic Press, Computer Vision and
Understanding, 1999, vol.75, pp.165-174.
[7] C.W.Ngo, T.C.Pong and R.T.Chin, "Exploiting image indexing
techniques in DCT domain", pattern Recognition, 2001, vol. 34, pp.
1841-1851.
[8] J.Jiang, A.Armstrong and G.C.Feng, "Direct content access and
extraction from JPEG compressed images", Pattern Recognition,
2002, vol. 35, pp. 2511-2519.
[9] G.Feng and J.Jiang, "JPEG compressed image retrieval via statistical
features", Pattern Recognition, 2003, vol. 36, pp. 977-985.
[10] S.Climer and S.K.Bhatia, "Image database indexing using JPEG
coefficients", Pattern Recognition, 2002, vol. 35, pp. 2479-2488.
[11] P.Ladret and A.G.Dugue, "Categorization and retrieval of scene
photographs from a JPEG compressed database", Pattern Analysis
and Applications, 2001, no. 4, pp. 185-199.
[12] J.Z.Wang, J.Li and G.Wiederhold, "SIMPLIcity: semantic sensitive
integrated matching for picture libraries", IEEE Trans. on Pattern
Analysis and Machine Intelligence, 2001, vol.23, no.9, pp.947-963.
[13] H.W.Yoo, S.H.Jung, D.H.Jang and Y.K.Na, "Extraction of major
object features using VQ clustering for content-based image
retrieval", Pattern Recognition, 2002, vol. 35, pp. 1115-1126.
[14] W.Pennebaker and J.Mitchell, "JPEG still image data compression
standard" , 1993, New York: Vann strand.
[15] A.Vailaya, A.K.Jain and H.J.Zhang, "On image classification: city vs.
landscape", Pattern Recognition, 1998, vol. 31, pp. 1921-1935.
[16] M.Szummer and R.W.Picard, "Indoor-outdoor image classification",
IEEE International Workshop on Content-Based Access of Image and
Video Database, in conj. With ICCV-98, Bombay, 1998.
[17] Y.Rubner, J.Puzicha, C.Tomasi and J.M.Buhmann, "Empirical
evaluation of dissimilarity measures for color and texture", Computer
Vision and Image Understanding, 2001, vol. 84, pp. 25-43.
@article{"International Journal of Information, Control and Computer Sciences:53877", author = "Hossein Nezamabadi-pour and Saeid Saryazdi", title = "Object-Based Image Indexing and Retrieval in DCT Domain using Clustering Techniques ", abstract = "In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.
", keywords = "Object-based image retrieval, DCT domain, Image indexing, Image classification.", volume = "1", number = "3", pages = "561-4", }
