Mohsen Ebrahimi Moghaddam
Articles written in Sadhana
Volume 38 Issue 4 August 2013 pp 571-589
Many colour constancy algorithms have been proposed to achieve a good performance in this ﬁeld. The gamut mapping algorithm is one of the most accurate and promising algorithms based on gamut assumption: illuminant can be estimated by comparing the colours distribution in the current image to acanonical gamut (i.e., a pre-learned distribution of colours). However, the gamut mapping algorithm is precise but it is time consuming. Therefore, some other methods such as GCIE (gamut constrained illuminant estimation) and CGM (cubical gamut mapping) have been proposed which work faster. However, the results of such methods are poor when the source light is not in the pre-deﬁned ones. In this paper, we propose an evolutionary algorithm for colour constancy based on gamut mapping assumption. This approach overcomes the mentioned problem in other gamut-based methods. The proposed evolutionary method uses a simple chromosome structure together with simple operators such as mutation, selection, and reproduction. Two versions of the proposed methods have been presented here. The ﬁrst one works on image pixels, while the second one tries on image derivative. The experiments were done on three different data sets that are used in literature and results were satisfactory. The results showed that the proposed method is much improved when compared to other related methods in most of the time especially in the case of real world images.
Volume 39 Issue 2 April 2014 pp 267-281
Colour constancy is defined as the ability to estimate the actual colours of objects in an acquired image disregarding the colour of scene illuminant. Despite large variety of existing methods, no colour constancy algorithm can be considered as universal. Among the methods, the gray framework is one of the best-known and most used approaches. This framework has some parameters that should be set with appropriate values to achieve the best performance for each image. In this article, we propose a neural network-based algorithm that aims to automatically determine the best value of gray framework parameters for each image. It is a multi-level approach that estimates the optimal values for the gray framework parameters based on relevant features extracted from the input image. Experimental results on two popular colour constancy datasets show an acceptable improvement over state-of-the-art methods.
Volume 43 Issue 9 September 2018 Article ID 0143
Recent increase in the number of digital photos in the content sharing and social networking websites has created an endless demand for techniques to analyze, navigate, and summarize these images. In this paper, we focus on image collection summarization. Earlier methods in image collection summarization consider representativeness and diversity criteria while recent ones also consider other criteria such as image quality, aesthetic or appeal. In this paper, we propose a multi-criteria context-sensitive approach for social image collection summarization. In the proposed method, two different sets of features are combined while each one looks at different criteria for image collection summarization: social attractiveness features and semantic features.The first feature set considers different aspects that make an image appealing such as image quality, aesthetic, and emotion to create attractiveness score for input images while the second one covers semantic content of images and assigns semantic score to them. We use social network infrastructure to identifyattractiveness features and domain ontology for extracting ontology features. The final summarization is provided by integrating the attractiveness and semantic features of input images. The experimental results on a collection of human generated summaries on a set of Flickr images demonstrate the effectiveness of the proposed image collection summarization approach.