2005
DOI: 10.1016/j.imavis.2005.07.015
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A histogram-based approach for object-based query-by-shape-and-color in image and video databases

Abstract: Cataloged from PDF version of article.Considering the fact that querying by low-level object features is essential in image and video data, an efficient approach for querying and retrieval by shape and color is proposed. The approach employs three specialized histograms, (i.e. distance, angle, and color histograms) to store feature-based information that is extracted from objects. The objects can be extracted from images or video frames. The proposed histogram-based approach is used as a component in the query… Show more

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Cited by 69 publications
(29 citation statements)
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“…The advantage of histogram is well reflected in this field especially for large database. The main types of image retrieval contain the texture image retrieval and the color image retrieval, on which many studies focus [15][16][17]. Here, we test the VBSD measure in these two retrieval tasks.…”
Section: The Performance Of Vbsd In Image Retrieval Experimentsmentioning
confidence: 99%
“…The advantage of histogram is well reflected in this field especially for large database. The main types of image retrieval contain the texture image retrieval and the color image retrieval, on which many studies focus [15][16][17]. Here, we test the VBSD measure in these two retrieval tasks.…”
Section: The Performance Of Vbsd In Image Retrieval Experimentsmentioning
confidence: 99%
“…In order to calculate the center of mass point, C m = (x Cm , y Cm ), of an object O, we use the following equation [18]:…”
Section: Calculating Object Featuresmentioning
confidence: 99%
“…There are numerous methods in the literature for comparing shapes [20,5,18,2,13]. The reader is especially referred to the surveys presented in [25,16] for good discussions on different techniques.…”
Section: Classification Metricmentioning
confidence: 99%
“…Some examples of prototype multimedia database systems include QBIC [2], VisualSeek [3], and VideoQ [4]. If the media is video, the queried features are spatial, spatio-temporal, semantic, motion (e.g., object trajectories) and object features (e.g., color, shape, texture) [5]. Before retrieval, the videos need to be processed to extract the features that can be queried.…”
Section: Introductionmentioning
confidence: 99%