&lt;title&gt;Content-based image retrieval using color tuple histograms&lt;/title&gt;

Rickman, R.; Stonham, T.J.

doi:10.1117/12.234772

Cited by 37 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [116], Rickman and Stonham proposed a color tuple histogram approach. They first constructed a code book which described every possible combination of coarsely quantized color hues that might be encountered within local regions in an image.…”

Section: Color Layoutmentioning

confidence: 99%

Image Retrieval: Current Techniques, Promising Directions, and Open Issues

Rui¹,

Huang²,

Chang³

1999

Journal of Visual Communication and Image Representation

1,574

776

View full text Add to dashboard Cite

This paper provides a comprehensive survey of the technical achievements in the research area of image retrieval, especially content-based image retrieval, an area that has been so active and prosperous in the past few years. The survey includes 100+ papers covering the research aspects of image feature representation and extraction, multidimensional indexing, and system design, three of the fundamental bases of content-based image retrieval. Furthermore, based on the state-of-the-art technology available now and the demand from real-world applications, open research issues are identified and future promising research directions are suggested. C

show abstract

Section: Color Layoutmentioning

confidence: 99%

Image Retrieval: Current Techniques, Promising Directions, and Open Issues

Rui¹,

Huang²,

Chang³

1999

Journal of Visual Communication and Image Representation

1,574

776

View full text Add to dashboard Cite

show abstract

“…Image feature selection basically includes color [1], shape [2], and texture [1]. Matching of image features determines how to compare the selected image features, including direct pixel comparison, sub-image comparison [1], histogram [3], singular value decomposition [1], and probability distribution [4]. Methods for evaluating similarity include Euclidean distance, Hausdorff distance [5], sum of square difference, and Mahalanobis distance [2].…”

Section: Introductionmentioning

confidence: 99%

Poster Image Matching by Color Scheme and Layout Information

Chen¹,

Kurozumi²,

Yamato³

2006

2006 IEEE International Conference on Multimedia and Expo

View full text Add to dashboard Cite

In this paper, we demonstrate a novel poster image matching system for wireless multimedia applications. We propose a method that incorporates both color and layout information of the poster image to achieve a robust performance in poster image matching. We apply both color compensation and background separation to extract a poster from an image effectively. Based on our experiment, we show that even under the effects of lighting, image rotation, scaling, and occlusion, our system can still maintain high recall and precision. We also show that our system can recognize the correct image from a database which contains several poster images with similar features. Finally, the promising performance of the poster image matching encourages us to further enrich the information retrieval for wireless environment.

show abstract

“…Rickman and Stonham [6] provide a method based on small equilateral triangles with fixed sides. Djeraba [7] tried to add the generalization capability for indexing and retrieval.…”

Section: Related Workmentioning

confidence: 99%

Using Intrinsic Object Attributes for Incremental Content Based Image Retrieval with Histograms

Park

Han

Kim

2007

Computational Science – ICCS 2007

View full text Add to dashboard Cite

Abstract. An incremental Content Based Image Retrieval (CBIR) method is proposed in this paper. This method is based on color histogram. Standard histograms, because of their efficiency and insensitivity to small changes, are widely used for content based image retrieval. We define an algorithm that utilizes the concept of Histogram Refinement [1] and we call it Color Refinement Method. Color Refinement method splits the pixels in a given bucket into several classes just like histogram refinement method. The classes are all related to colors and are based on color coherence vectors. After the calculation of clusters using color refinement method, inherent features of each of cluster is calculated. These inherent features are used for incremental CBIR.

show abstract

<title>Content-based image retrieval using color tuple histograms</title>

Cited by 37 publications

References 0 publications

Image Retrieval: Current Techniques, Promising Directions, and Open Issues

Image Retrieval: Current Techniques, Promising Directions, and Open Issues

Poster Image Matching by Color Scheme and Layout Information

Using Intrinsic Object Attributes for Incremental Content Based Image Retrieval with Histograms

Contact Info

Product

Resources

About