Object-Based Image Indexing and Retrieval in DCT Domain using Clustering Techniques
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Object-Based Image Indexing and Retrieval in DCT Domain using Clustering Techniques

Authors: Hossein Nezamabadi-pour, Saeid Saryazdi

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1059809

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References:


[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.