Background: Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. Cheng and Church have introduced a measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we review basic concepts of the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP)-construction and local search phases and propose a new method which is a variant of GRASP called Reactive Greedy Randomized Adaptive Search Procedure (Reactive GRASP) to detect significant biclusters from large microarray datasets. The method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using the Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list is self-adjusted, depending on the quality of the solutions found previously.

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Cultural dances are practiced all over the world. The study of various gestures of the performer using computer vision techniques can help in better understanding of these dance forms and for annotation purposes. Bharatanatyam is a classical dance that originated in South India. Bharatanatyam performer uses hand gestures (mudras), facial expressions and body movements to communicate to the audience the intended meaning. According to Natyashastra, a classical text on Indian dance, there are 28 Asamyukta Hastas (single-hand gestures) and 23 Samyukta Hastas (Double-hand gestures) in Bharatanatyam. Open datasets on Bharatanatyam dance gestures are not presently available. An exhaustive open dataset comprising of various mudras in Bharatanatyam was created. The dataset consists of 15[Formula: see text]396 distinct single-hand mudra images and 13[Formula: see text]035 distinct double-hand mudra images. In this paper, we explore the dataset using various multidimensional visualization techniques. PCA, Kernel PCA, Local Linear Embedding, Multidimensional Scaling, Isomap, t-SNE and PCA–t-SNE combination are being investigated. The best visualization for exploration of the dataset is obtained using PCA–t-SNE combination.

Shadows create significant problems in many computer vision and image analysis tasks such as object recognition, object tracking, and image segmentation. For a machine, it is very difficult to distinguish between a shadow and a real object. As a result, an object recognition system may incorrectly recognize a shadow region as an object. So the detection of shadows in images will enhance the performance of many machine vision tasks. This paper implements a shadow detection method, which is based on Tricolor Attenuation Model (TAM) enhanced with adaptive histogram equalization (AHE). TAM uses the concept of intensity attenuation of pixels in the shadow region which is different for the three color channels. It originates from the idea that if the minimum attenuated color channel is subtracted from the maximum attenuated one, the shadow areas become darker in the resulting TAM image. But this resulting image will be of low contrast due to the high correlation among R, G and B color channels. In order to enhance the contrast, adaptive histogram equalization is used. The incorporation of AHE significantly improved the quality of the detected shadow region.