About: Histogram equalization is a(n) research topic. Over the lifetime, 5755 publication(s) have been published within this topic receiving 89313 citation(s).
Papers published on a yearly basis
01 Aug 1994-Graphics gems
••17 Jun 1997
TL;DR: Experimental evidence suggests that this new image feature called the color correlogram outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.
Abstract: We define a new image feature called the color correlogram and use it for image indexing and comparison. This feature distills the spatial correlation of colors, and is both effective and inexpensive for content-based image retrieval. The correlogram robustly tolerates large changes in appearance and shape caused by changes in viewing positions, camera zooms, etc. Experimental evidence suggests that this new feature outperforms not only the traditional color histogram method but also the recently proposed histogram refinement methods for image indexing/retrieval.
TL;DR: Two new color indexing techniques are described, one of which is a more robust version of the commonly used color histogram indexing and the other which is an example of a new approach tocolor indexing that contains only their dominant features.
Abstract: We describe two new color indexing techniques. The first one is a more robust version of the commonly used color histogram indexing. In the index we store the cumulative color histograms. The L1-, L2-, L(infinity )-distance between two cumulative color histograms can be used to define a similarity measure of these two color distributions. We show that this method produces slightly better results than color histogram methods, but it is significantly more robust with respect to the quantization parameter of the histograms. The second technique is an example of a new approach to color indexing. Instead of storing the complete color distributions, the index contains only their dominant features. We implement this approach by storing the first three moments of each color channel of an image in the index, i.e., for a HSV image we store only 9 floating point numbers per image. The similarity function which is used for the retrieval is a weighted sum of the absolute differences between corresponding moments. Our tests clearly demonstrate that a retrieval based on this technique produces better results and runs faster than the histogram-based methods.
TL;DR: It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.
Abstract: Histogram equalization is widely used for contrast enhancement in a variety of applications due to its simple function and effectiveness. Examples include medical image processing and radar signal processing. One drawback of the histogram equalization can be found on the fact that the brightness of an image can be changed after the histogram equalization, which is mainly due to the flattening property of the histogram equalization. Thus, it is rarely utilized in consumer electronic products such as TV where preserving the original input brightness may be necessary in order not to introduce unnecessary visual deterioration. This paper proposes a novel extension of histogram equalization to overcome such a drawback of histogram equalization. The essence of the proposed algorithm is to utilize independent histogram equalizations separately over two subimages obtained by decomposing the input image based on its mean with a constraint that the resulting equalized subimages are bounded by each other around the input mean. It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.
••01 Feb 1997
TL;DR: It is shown that CCV’s can give superior results to color histogram-based methods for comparing images that incorporates spatial information, and to whom correspondence should be addressed tograms for image retrieval.
Abstract: Color histograms are used to compare images in many applications. Their advantages are efficiency, and insensitivity to small changes in camera viewpoint. However, color histograms lack spatial information, so images with very different appearances can have similar histograms. For example, a picture of fall foliage might contain a large number of scattered red pixels; this could have a similar color histogram to a picture with a single large red object. We describe a histogram-based method for comparing images that incorporates spatial information. We classify each pixel in a given color bucket as either coherent or incoherent, based on whether or not it is part of a large similarly-colored region. A color coherence vector (CCV) stores the number of coherent versus incoherent pixels with each color. By separating coherent pixels from incoherent pixels, CCV’s provide finer distinctions than color histograms. CCV’s can be computed at over 5 images per second on a standard workstation. A database with 15,000 images can be queried for the images with the most similar CCV’s in under 2 seconds. We show that CCV’s can give superior results to color his∗To whom correspondence should be addressed tograms for image retrieval.
Related Topics (5)
111.8K papers, 2.1M citations
Feature (computer vision)
128.2K papers, 1.7M citations
79.6K papers, 1.8M citations
229.9K papers, 3.5M citations
Convolutional neural network
74.7K papers, 2M citations