Image coding by adaptive tree-structured segmentation

Wu, Xiangyou; Yao, Chengfu

doi:10.1109/dcc.1991.213374

Cited by 15 publications

(17 citation statements)

References 8 publications

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“…To construct the SSM, we first segment the image using a simple, easy to implement, binary image tree (bintree) adaptive image segmentation method [8], which can be regarded as a much simplified version of binary space partitioning tree image segmentation method [4][5][6]. An image (assumed rectangular in shape) is first cut into two equal sized sub-images by either a vertical or a horizontal straight line.…”

Section: Scene Structural Matrixmentioning

confidence: 99%

Retrieving Landscape Images Using Scene Structural Matrix

Qiu

Sudirman

2001

Advances in Multimedia Information Processing — PCM 2001

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Abstract. In this paper, we present Scene Structural Matrix (SSM) and apply it to the retrieval of landscape images. The SSM captures the overall structural characteristics of the scene by indexing the geometric features of the image. A binary image tree (bintree) is used to partition the image and from which we derive multi-resolution geometric structural descriptors of the image. It is shown that SSM is particularly effective in retrieving images with strong structural features, such as landscape photographs. We show that SSM is robust against spatial and spectral distortions thus making it superior to current state of the art techniques such as color correlogram in certain applications. We will also show that images retrieved by the SSM are more relevant than those returned by color correlogram and color histogram.

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Section: Scene Structural Matrixmentioning

confidence: 99%

Retrieving Landscape Images Using Scene Structural Matrix

Qiu

Sudirman

2001

Advances in Multimedia Information Processing — PCM 2001

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“…Quadtree-based image compression, which recursively divides the image into simple geometric regions, has been one of the most popular segmentation-based coding schemes investigated by researchers [15], [25], [27], [29], [33]. Leonardi et al [15] utilized the classic split and merge segmentation techniques to extract image regions and then approximate the contours and image characteristics of those regions.…”

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confidence: 99%

Rate-distortion optimized tree-structured compression algorithms for piecewise polynomial images

Shukla

Dragotti

et al. 2005

IEEE Trans. on Image Process.

142

108

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Abstract-This paper presents novel coding algorithms based on tree-structured segmentation, which achieve the correct asymptotic rate-distortion (R-D) behavior for a simple class of signals, known as piecewise polynomials, by using an R-D based prune and join scheme. For the one-dimensional case, our scheme is based on binary-tree segmentation of the signal. This scheme approximates the signal segments using polynomial models and utilizes an R-D optimal bit allocation strategy among the different signal segments. The scheme further encodes similar neighbors jointly to achieve the correct exponentially decaying R-D behavior ( ( ) 0 2 ), thus improving over classic wavelet schemes. We also prove that the computational complexity of the scheme is of ( log ). We then show the extension of this scheme to the two-dimensional case using a quadtree. This quadtree-coding scheme also achieves an exponentially decaying R-D behavior, for the polygonal image model composed of a white polygon-shaped object against a uniform black background, with low computational cost of ( log ). Again, the key is an R-D optimized prune and join strategy. Finally, we conclude with numerical results, which show that the proposed quadtree-coding scheme outperforms JPEG2000 by about 1 dB for real images, like cameraman, at low rates of around 0.15 bpp.

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“…To construct the SSM, we first segment the image using a simple, easy to implement, binary image tree (bintree) adaptive image segmentation method [8], which can be regarded as a much simplified version of binary space partitioning tree image segmentation method [4][5][6]. An image (assumed rectangular in shape) is first cut into two equal sized subimages by either a vertical or a horizontal straight line.…”

Section: Binary Tree Image Segmentationmentioning

confidence: 99%

Spectral and Spatial Invariant Image Retrieval using Scene Structural Matrix

Qiu¹,

Sudirman²

2001

Procedings of the British Machine Vision Conference 2001

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We introduce Scene Structural Matrix (SSM), a novel image content descriptor and its application to invariant image retrieval. The SSM captures the overall structural characteristics of the scene by indexing the geometric features of the image. We employ a binary image tree (bintree) to partition the image and from which we derive multiscale geometric structural descriptors of the image. We have applied the SSM to contentbased image retrieval from image databases. Experimental results show that SSM is particularly effective in retrieving images with strong structural features, such as landscape photographs. We will show that SSM is robust against spatial and spectral distortions thus making it superior to current state of the art techniques such as colour correlogram in certain applications. We will also show that images retrieved by the SSM are more relevant than those returned by colour correlogram and colour histogram.

show abstract

Image coding by adaptive tree-structured segmentation

Cited by 15 publications

References 8 publications

Retrieving Landscape Images Using Scene Structural Matrix

Retrieving Landscape Images Using Scene Structural Matrix

Rate-distortion optimized tree-structured compression algorithms for piecewise polynomial images

Spectral and Spatial Invariant Image Retrieval using Scene Structural Matrix

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