Image Processing
TL;DR: The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images.
Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.
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Citations
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Cites methods from "Image Processing"
...system makes us of an isotropic bandpass decomposition derived from application of Laplacian of Gaussian filters [25], [29] to the image data....
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...In practice, the filtered image is realized as a Laplacian pyramid [8], [29]....
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Cites background or methods from "Image Processing"
...Structural description of chromosome shape (reprinted from [14])....
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...Common invariants include (i) geometric invariants such as cross-ratio, length ratio, distance ratio, angle, area [69], triangle [70], invariants from coplanar points [14]; (ii) algebraic invariants such as determinant, eigenvalues [71], trace [14]; (iii) di<erential invariants such as curvature, torsion and Gaussian curvature....
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...Designers of shape invariants argue that although most of other shape representation techniques are invariant under similarity transformations (rotation, translation and scaling), they depend on viewpoint [14]....
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...The extracting of the convex hull can use both boundary tracing method [14] and morphological methods [11,15]....
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...Assuming the shape boundary has been represented as a shape signature z(i), the rth moment mr and central moment r can be estimated as [14]...
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References
18,474 citations
8,403 citations
5,812 citations
2,283 citations
2,009 citations