Semantics-sensitive Retrieval for Digital Picture Libraries

Abstract

This paper presents an algorithm to segment images into four classes: background, photograph, text and graph. There are two important aspects about the algorithm. The rst is that the algorithm takes a multiscale approach, which adaptively classi es an image at di erent resolutions.

A Survey on Content-based Image Retrieval

“…As reported in [95,92,93,94,20], the closest colour spaces to human perception include RGB, LUV, HSV, HMMD, YCrCb, and LAB. Also, various colour descriptors/features, such as colour histogram, colour moments, colour-covariance matrix, and colour coherence vector have been proposed for CBIR systems [96,97,98]. Similarly, in [99], colour structure, dominant colour, colour layout and scalable colour have been proposed as standard MPEG-7 colour features.…”

“…Despite these efforts to encode the colour properties of the image, the proposed features have shown limitations to express image high level semantic. In order to alleviate this concern, researchers proposed averaging colour of all pixels in a region/image as a colour feature [20,98,100]. However, this feature is affected by the image segmentation quality.…”

“…Similarly, statistical features such as wold features [105] and Tamura texture features [106] have been proposed in order to represent image visual content better and improve CBIR accuracy. Later, MPEG-7 adopted some statistical measures proposed in [106], such as directionality, regularity and coarseness, to dene standard texture browsing descriptor [94,98]. However, this statistic measure based features are not robust to scale and orientation variation [107].…”

“…Based on researcher contributions to propose accurate CBIR systems, wavelet and Gabor based texture features proved to match the best human vision and achieved the highest performance [98,104,108]. However, one should notice that these two texture features are sensitive to the shape of the image region [20,104].…”

An overview of content-based image retrieval techniques (CBIR)

“…Image retrieval bases on these features a set of vectors of images are available. Gabor filters are used to extract frequency information [3], histogram edge color to extract the edge information [4], and the color histogram to extract color information in color images [5] and so on [2]. CBIR focuses on the characteristics of the image that provides the ability to query and recently has been focus on the study of the image database.…”