In image classification, obtaining adequate data to learn a robust classifier has often proven to be difficult in several scenarios. Classification of histological tissue images for health care analysis is a notable application in this context due to the necessity of surgery, biopsy or autopsy. To adequately exploit limited training data in classification, we propose a saliency guided dictionary learning method and subsequently an image similarity technique for histo-pathological image classification. Salient object detection from images aids in the identification of discriminative image features. We leverage the saliency values for the local image regions to learn a dictionary and respective sparse codes for an image, such that the more salient features are reconstructed with smaller error. The dictionary learned from an image gives a compact representation of the image itself and is capable of representing images with similar content, with comparable sparse codes. We employ this idea to design a similarity measure between a pair of images, where local image features of one image, are encoded with the dictionary learned from the other and vice versa. To effectively utilize the learned dictionary, we take into account the contribution of each dictionary atom in the sparse codes to generate a global image representation for image comparison. The efficacy of the proposed method was evaluated using three tissue data sets that consist of mammalian kidney, lung and spleen tissue, breast cancer, and colon cancer tissue images. From the experiments, we observe that our methods outperform the state of the art with an increase of 14.2% in the average classification accuracy over all data sets.

Present investigation has made an attempt to assess the variations in physicochemical status of two urban ponds (constructed boundary: Pond A and natural boundary: Pond B) of Chandannagar and also established the variations in the diversity of macrophytes and zooplankton of the study areas during winter (Nov-Dec, 2017). Limnological parameters recorded from two spots collected in three phases, such as temperature, pH, transparency, conductivity, DO, BOD, total dissolved solids (TDS), total suspended solids (TSS), total alkalinity, total hardness and chloride, were evaluated. From this observation, it is reported that there are marked variations in different water quality parameters of two urban ponds during the study period. The value of water temperature in Pond A and B was 22.0 and 21.3 °C, respectively. pH value (7.9) was highest in Pond B. Pond B water was more turbid than Pond A. The highest values of other parameters like conductivity (218.50 µS/cm) DO (8.47 mg/l), BOD (5.69 mg/l), TDS (135.19 mg/l), TSS (67.60 mg/l), total alkalinity (181.15 mg/l), total hardness (145.66 mg/l) and chloride (58.94 mg/l) were found in case of Pond B. Maximum macrophytic vegetations were found in Pond B in comparison with Pond A. The study of the zooplankton community reveals that the maximum occurrence of rotifers in Pond B indicates pollution status.

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In this work, we develop algorithms for tracking time sequences of sparse spatial signals with slowly changing sparsity patterns, and other unknown states, from a sequence of nonlinear observations corrupted by (possibly) non-Gaussian noise. A key example of the above problem occurs in tracking moving objects across spatially varying illumination changes, where motion is the small dimensional state while the illumination image is the sparse spatial signal satisfying the slow-sparsity-pattern-change property.Index Terms-particle filtering, compressed sensing, tracking INTRODUCTIONWe study the problem of tracking (causally estimating) a time sequence of sparse spatial signals with slowly changing sparsity patterns, as well as other unknown states, from a sequence of nonlinear observations corrupted by (possibly) non-Gaussian noise. In many practical applications, the unknown state can be split into a small dimensional part and a spatial signal (large dimensional part). The spatial signal is often well modeled as being sparse in some domain. For a long sequence, its sparsity pattern can change over time, although the changes are usually slow. A key example of the above problem occurs in tracking moving objects across spatially varying illumination changes, e.g. persons walking under a tree (different lighting falling on different parts of the face due to the leaves blocking or not blocking the sunlight and this pattern changes with time as the leaves move) or in indoor sequences with variable lighting. In all these cases, one needs to explicitly track the motion (small dimensional part) as well as the illumination "image" (illumination at each pixel in the image), which is the spatial signal satisfying the slow-sparsity-pattern-change property [see Sec 4].Related Work. In recent years, starting with the seminal papers of Candes, Romberg, Tao and of Donoho [1,2] there has been a large amount of work on sparse signal recovery / compressive sensing (CS). The problem of recursively recovering a time sequence of sparse signals, with slowly changing sparsity patterns and signal values, from linear measurements has also been extensively studied [3,4,5,6,7,8,9,10,11,12,13,14,15].For tracking problems that need to causally estimate a time sequence of hidden states, Xt, from nonlinear and possibly nonGaussian measurements, Yt, the most common and efficient solution is to use a particle filter (PF). The PF uses sequential importance sampling [16] along with a resampling step [17] to obtain a sequential Monte Carlo estimate of the posterior distribution, f X t |Y 1:t (xt|y1:t), of the state Xt conditioned on all observations up to the current time, Y1:t. In our problem, part of the state vector is a discrete spatial signal and hence very high dimensional. As a result, in this case, the original PF [17] will require too many particles for accurate tracking and hence becomes impractical to use. As

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