<title>Content-based image retrieval using multiresolution histogram representation</title>

Abstract

We present two new approaches based on color histogram indexing for content-based retrieval in image databases. Since the high computational complexity has been one of the main barriers towards the use of similarity measures such as histogram intersection in large databases, we propose a hierarchical indexing scheme where computationally efficient features are used to subset the images before more sophisticated techniques are applied for precise retrieval. The use of histograms at different color resolutions as filtering and matching features in a hierarchical scheme is studied. In the second approach, a multiresolution representation of the histogram using the indices and signs of its largest wavelet coefficients is examined. Excellent results have been observed using the latter method.Keywords: color indexing, color histogram, multiresolution, content-based retrieval. 0-8194-1970-2/95/$6.OO Downloaded From: http://proceedings.spiedigitallibrary.org/ on 05/18/2015 Terms of Use: http://spiedl.org/terms

Human factors in color-based image retrieval: an empirical study on size estimate accuracies

“…In fact, content-based image retrieval by color features has been frequently used (Binaghi et al, 1994;Gong et al, 1994;Lu et al, 1994;Ng et al, 1995;Phanendra Babu et al, 1995;Sawhney and Hafner, 1994;Stricker and Swain, 1994;Vellaikal and Kuo, 1995).…”

“…Techniques and algorithms that can extract ''visual features'' from images are crucial in supporting content-based retrieval. Color histogram techniques have been widely used (Brunelli and Mich, 2001;Cinque et al, 2001;Gong et al, 1994;Lu et al, 1994;McDonald et al, 2001;Mehtre et al, 1995;Ng et al, 1995;Niblack et al, 1993;Phanendra Babu et al, 1995;Rickman and Stonham, 1996;Sawhney and Hafner, 1994;Stricker and Orengo, 1995;Swain and Ballard, 1991;Vellaikal and Kuo, 1995;Yoo et al, 2002). Essentially, color histogram is used to represent the color distribution of an image.…”

Perceived image similarity and quantization resolution

“…Various quantization resolutions have been used, from 16 to 4096 color bins [32,36,49,51]. For a long time, researchers have to choose an appropriate resolution so that they could reach a balance between the performance and the efficiency of the system.…”

“…Two colors are the same if they fall into the same bin. Different quantization resolutions have been used, ranging from 64 to 4096 [9,19,32,49,52,51].…”

<title>Color distribution analysis and quantization for image retrieval</title>

“…Feature-based indexing techniques such as those using low-level image features such as colors, textures and shapes have been studied and found useful in image retrieval [1] . Different measures on the color feature such as color histograms, prominent colors and salient colors have proven to be efficient in discriminating between relevant and non-relevant images [2][3][4][5][6] . In particular, retrieval based on histograms has been widely studied and is now considered to be an very effective measure for color image retrieval based on queries by example.…”