&lt;title&gt;Color-WISE: a system for image similarity retrieval using color&lt;/title&gt;

Sethi, Ishwar K.; Coman, Ioana L.; Day, Brian L.; Jiang, Fuhua; Li, Dongge; Segovia-Juárez, José; Wang, Gang; You, Byoung-Moon

doi:10.1117/12.298438

Cited by 29 publications

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“…Different similarity measures have been suggested in the literature to compare images 3,11 . We are using in our clustering algorithm the similarity measure that, besides the perceptual similarity between different bins of a color histogram, recognizes the fact that human perception is more sensitive to changes in hue values 7,12 . It also recognizes that human perception is not proportionally sensitive to changes in hue value.…”

Section: Color Similarity Metricmentioning

confidence: 99%

<title>Mapping low-level image features to semantic concepts</title>

Stan

Sethi

2001

SPIE Proceedings

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Humans tend to use high-level semantic concepts when querying and browsing multimedia databases; there is thus, a need for systems that extract these concepts and make available annotations for the multimedia data. The system presented in this paper satisfies this need by automatically generating semantic concepts for images from their low-level visual features. The proposed system is built in two stages. First, an adaptation of k-means clustering using a non-Euclidean similarity metric is applied to discover the natural patterns of the data in the low-level feature space; the cluster prototype is designed to summarize the cluster in a manner that is suited for quick human comprehension of its components. Second, statistics measuring the variation within each cluster are used to derive a set of mappings between the most significant low-level features and the most frequent keywords of the corresponding cluster. The set of the derived rules could be used further to capture the semantic content and index new untagged images added to the image database. The attachment of semantic concepts to images will also give the system the advantage of handling queries expressed in terms of keywords and thus, it reduces the semantic gap between the user's conceptualization of a query and the query that is actually specified to the system. While the suggested scheme works with any kind of low-level features, our implementation and description of the system is centered on the use of image color information. Experiments using a 21 00 image database are presented to show the efficacy of the proposed system.

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Section: Color Similarity Metricmentioning

confidence: 99%

<title>Mapping low-level image features to semantic concepts</title>

Stan

Sethi

2001

SPIE Proceedings

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“…Wolf [8] provided an algorithm to detect the key frames in MPEG video file format using optical flow motion analysis. Sethi et al [9] used a similarity metric, which matches both the hue and saturation components of the HSI color space between two images. Pass et al [10] defined a notion of Joint Histograms.…”

Section: Related Workmentioning

confidence: 99%

Developing video services for mobile users

Ahmed

Impey

Karmouch

2003

36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of The

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“…Section 4 presents our methodology of computing the spatial color index for image retrieval as well as the similarity function for image comparison. In Section 5, we compare the proposed approach with two of the best performing of recent spatial color indexing schemes, Color-WISE [22] and the color correlogram [11] approaches, over two databases that comprise a total of 3388 images, to demonstrate the effectiveness and efficiency of our proposed approach. Finally, we give some concluding remarks.…”

Section: Introductionmentioning

confidence: 99%

Untitled

Grosky

2001

Multimedia Tools and Applications

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As color plays an essential role in image composition, many color indexing techniques have been studied for content-based image retrieval. This paper examines the use of a computational geometry-based spatial color indexing methodology for effective and efficient image retrieval. In this scheme, an image is evenly divided into a number of M * N non-overlapping blocks, and each individual block is abstracted as a unique feature point labeled with its spatial location and dominant colors. For each set of feature points labeled with the identical color, we construct a Delaunay triangulation and then compute the feature point histogram by discretizing and counting the angles produced by this triangulation. The concatenation of all these feature point histograms serves as the image index, the so-called color anglogram. An important contribution of this work is to encode the spatial color information using geometric triangulation, which is rotation, translation, and scale invariant. We have compared the proposed approach with two of the best performing of recent spatial color indexing schemes, Color-WISE and the color correlogram approaches, respectively, at image block and pixel levels of different granularity. Various experimental results demonstrate the efficacy of our techniques.

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<title>Color-WISE: a system for image similarity retrieval using color</title>

Cited by 29 publications

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<title>Mapping low-level image features to semantic concepts</title>

<title>Mapping low-level image features to semantic concepts</title>

Developing video services for mobile users

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