One step beyond histograms: Image representation using Markov stationary features

Li, Jianguo; Wu, Weixin; Wang, Tao; Zhang, Yimin

doi:10.1109/cvpr.2008.4587839

Cited by 19 publications

(8 citation statements)

References 24 publications

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“…Supposing there are two images named A and B with m corresponding states, the similarity is computed with the parameter W and Π, where W is the vector denoting state feature weight of corresponding states and Π is the stationary distribution of the corresponding states in [18] w , w , … , w and Π π , π , … , π . In some case, the users are only interested in a portion of the image, and the rest is unregarded.…”

Section: Similarity Measurementmentioning

confidence: 99%

“…Spatiogram [17] is a generalization of color histogram, which captures spatial information by constructing the higher order moments. The Markov chain model [18][19][20] is also employed to characterize spatial information between pixels in different colors.…”

Section: Introductionmentioning

confidence: 99%

“…To fit different kinds of images, this global descriptor tends to have large structure and therefore intense computation is required during image comparison. For instance, a vector with the dimension of 166 is used to describe images in [18], and typical color histogram usually quantizes color space into more than 1000 states. Some methods have been proposed for matching high dimensional histograms [21], but further improvements are still expected.…”

Section: Introductionmentioning

confidence: 99%

“…Step 3: The stationary distribution Π about the cooccurrence matrices of the two images are computed according to [18]. Π ∑ a m (8) where A a , … , a I T T ⋯ T n 1 ⁄ .…”

Section: Similarity Measurementmentioning

confidence: 99%

See 3 more Smart Citations

Content based Image Retrieval with Color Invariants

Song¹,

Xiao²,

Qu³

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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Abstract-Content based image retrieval (CBIR) is an essential task in many applications. Color based methods have received much attention in past years, since color could serve efficiently for image retrieval, especially in the case of large database. However, there are two main drawbacks for color based image retrieval methods. Firstly, color based methods are not suitable for similar scenes under different illumination conditions, because color is sensitive to illumination. Secondly, existing approaches usually employ image descriptors with large size, which makes the approach unsuitable for real-time application. To overcome drawbacks mentioned above, an adaptive image retrieval method has been proposed, which integrates the color invariant with the spatial information about images. Different from previous methods, the quantization of the color space has not been manually determined. Instead, it has been decided according to the content of image, using an adaptive clustering technique. Therefore, the size of image descriptor is very small.In the proposed method, feature maps for images have been firstly established, which consist of color invariants. And then the Markov chain model has been employed to capture color information and spatial features. Finally, similar images are retrieved based on two-stage weighted distance. Experimental results show that the proposed method has improved simplicity and compactness of color based image retrieval methods, without the loss of efficiency and robustness.

show abstract

Section: Similarity Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Step 3: The stationary distribution Π about the cooccurrence matrices of the two images are computed according to [18]. Π ∑ a m (8) where A a , … , a I T T ⋯ T n 1 ⁄ .…”

Section: Similarity Measurementmentioning

confidence: 99%

See 2 more Smart Citations

Content based Image Retrieval with Color Invariants

Song¹,

Xiao²,

Qu³

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

View full text Add to dashboard Cite

show abstract

“…Spatiogram [17] is a generalization of the color histogram, and captures spatial information by constructing the higher order moments. The hidden Markov chain model [18][19][20] is also employed to characterize spatial information between colors.…”

Section: Introductionmentioning

confidence: 99%

A statistical image retrieval method using color invariant

Cheng

2009

2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation - (CIRA)

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Content based image retrieval is an essential task in many image processing applications, among which, color based methods have been receiving constant attentions in past years, because color information is a discriminative descriptor for image retrieval, especially in case of large database. A limitation of previous color based methods is their unsuitability for retrieving similar scenes under varying lighting conditions as color is sensitive to illuminations. Besides image descriptors of some existing methods are with large dimensionality and thus computational expensive. As betterment, an adaptive method is proposed in this paper, which integrates the color invariant with some spatial information of images. Different from previous work, the number of states during the quantization of the color space is not manually determined. Instead, it depends on the context of the image itself, using an adaptive clustering technique: Firstly, feature map consisting of color invariants is established for images. Secondly, the Markov chain model is employed to capture the image both color and spatial information. Thirdly, an image descriptor is computed for each image, not under the frame of the entire fixed color space. To practice our method, similar images are retrieved with a similarity measure based on a two-stage weighted distance. Experiments show that, this method has improved simplicity and compactness without the lost of efficiency and robustness.

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Recognition of occluded objects by reducing feature interactions

Lim

Jia

2012

Image and Vision Computing

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One step beyond histograms: Image representation using Markov stationary features

Cited by 19 publications

References 24 publications

Content based Image Retrieval with Color Invariants

Content based Image Retrieval with Color Invariants

A statistical image retrieval method using color invariant

Recognition of occluded objects by reducing feature interactions

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