Abstract

Intrinsic Image Decomposition (IID) is a challenging and interesting computer vision problem with various applications in several fields. We present novel semantic priors and an integrated approach for single image IID that involves analyzing image at three hierarchical context levels. Local context priors capture scene properties at each pixel within a small neighborhood. Mid-level context priors encode object level semantics. Global context priors establish correspondences at the scene level. Our semantic priors are designed on both fixed and flexible regions, using selective search method and Convolutional Neural Network features. Experiments and analysis of our method indicate the utility of our weak semantic priors and structured hierarchical analysis in an IID framework. We compare our method with the current state-of-the-art and show results with lesser artifacts. Finally, we highlight that proper choice and encoding of prior knowledge can produce competitive results compared to end-to-end deep learning IID methods, signifying the importance of such priors. We believe that the insights and techniques presented in this paper would be useful in the future IID research.

Abstract

Replacing overexposed or dull skies in outdoor photographs is a desirable photo manipulation. It is often necessary to color correct the foreground after replacement to make it consistent with the new sky. Methods have been proposed to automate the process of sky replacement and color correction. However, many times a color correction is unwanted by the artist or may produce unrealistic results. We propose a data-driven approach to sky-replacement that avoids color correction by finding a diverse set of skies that are consistent in color and natural illumination with the query image foreground. Our database consists of ∼ 1200 natural images spanning many outdoor categories. Given a query image, we retrieve the most consistent images from the database according to L2 similarity in feature space and produce candidate composites. The candidates are re-ranked based on realism and diversity. We used pre-trained CNN features and a rich set of hand-crafted features that encode color statistics, structural layout, and natural illumination statistics, but observed color statistics to be the most effective for this task. We share our findings on feature selection and show qualitative results and a user-study based evaluation to show the effectiveness of the proposed method.

Abstract

t. In addition to maximizing the revenue, retailers also aim at diversifying product offerings for facilitating sustainable revenue generation in the long run. Thus, it becomes a necessity for retailers to place appropriate itemsets in a limited k number of premium slots in retail stores for achieving the goals of revenue maximization and itemset diversification. In this regard, research efforts are being made to extract itemsets with high utility for maximizing the revenue, but they do not consider itemset diversification i.e., there could be duplicate (repetitive) items in the selected top-utility itemsets. Furthermore, given utility and support thresholds, the number of candidate itemsets of all sizes generated by existing utility mining approaches typically explodes. This leads to issues of memory and itemset retrieval times. In this paper, we present a framework and schemes for efficiently retrieving the top-utility itemsets of any given itemset size based on both revenue as well as the degree of diversification. Here, higher degree of diversification implies less duplicate items in the selected top-utility itemsets. The proposed schemes are based on efficiently determining and indexing the topλ high-utility and diversified itemsets. Experiments with a real dataset show the overall effectiveness and scalability of the proposed schemes in terms of execution time, revenue and degree of diversification w.r.t. a recent existing scheme.

Abstract

Deep convolutional features for word images and textual embedding schemes have shown great success in word spotting. In this work, we follow these motivations to propose an End2End embedding framework which jointly learns both the text and image embeddings using state of the art deep convolutional architectures. The three major contributions of this work are: (i) an End2End embedding scheme to learn a common representation for word images and its labels, (ii) building a state of art word image descriptor and demonstrating its utility as off-the-shelf features for word spotting, and (iii) use of synthetic data as a complementary modality to further enhance word spotting and recognition. On the challenging IAM handwritten dataset, we report a mAP of 0.9509 for query-by-string based retrieval task. Under lexicon based word recognition, our proposed method report a 2.66 and 5.10 CER and WER respectively.

Abstract

Understanding and analyzing the changes in vegetation cover is very important in several aspects including climatic changes, water budget, ecological balance and specially to undertake necessary conservation measures. The concept of neural network has gained much significance in the analysis of vegetation dynamics using remote sensing satellite data. In the current study an attempt has been made to predict the vegetation dynamics using MODIS NDVI time series data sets and long short term memory network, an advanced technique adapted from the artificial neural network. The dataset of 861 NDVI images from January 2000 to June 2016 is used for making the time series. The data is segregated into three sets which comprises of training set (70%), validation set (20%), and testing set (10%). To check the reliability of the experiment we have finalised two different regions after extensive research for investigation. These include different terrains in the Great Nicobar Islands, one region along the coast where vegetation has severe ecological damage due to 2004 Indian Ocean tsunami and the other, an interior region which remained imperturbable during the tsunami. Long short term memory network, an advanced neural network is trained with these NDVI values for both the regions separately to predict the future vegetation dynamics. To measure the accuracy of the LSTM network, root mean square error is calculated. The resulting plots from both the experiments indicate that the long short-term memory neural network follows the series in addition to coinciding with the required time series. Also, an unanticipated change in the trend of the NDVI series were well adapted by the network and was able to predict the future NDVI values with good accuracy maintaining RMSE less than 0.03 without providing any supplementary data. By adopting the prescribed method in the paper, anticipation of vegetation changes can be done accurately much ahead of time and take proactive measures accordingly to safeguard and improve the vegetation in any area.

Abstract

In many cities, it is difficult for authorities to build road monitoring systems that are cost-friendly and effective. The existing road monitoring systems include heavy machinery with sensors fitted which make them expensive for authorities to use for frequent monitoring. To address this challenge, we propose an innovative collaborative system for road condition monitoring leveraging the existing technologies by data fusion approach. The mobile application on smart-phones of geo-citizens coordinates the data collection and reports back to a central server for assimilation and further processing. Image data is processed to obtain the road surface while the accelerometer and GPS data is used for identifying road condition and location information. This data is then fused based on location and timestamp information and stored as segments in the database. As the data obtained is crowd-sourced, there are multiple tracks for the same segment of the road and this segmented approach allows for collating multiple user-provided information and validation. In case there is multiple track information available for a particular segment, a ranking model based on the number of collaborators is used to rank the status. The final database holds the information like segmentId, timestamp, GPS coordinates and status of the segment. This data serves as an input to the visualization service. The multiplicity of data points and anonymity of the users establishes robustness and crowd-sourced model ensures frequent and continuous coverage.

Abstract

Barak valley is an area in northeast part of India where traditionally practice of shifting cultivation is more prevalent than sedentary agriculture. In addition, population growth coupled with the geographic isolation of the area has further aggravated the decline of forest cover in the area. The three districts of Assam that cover the Barak valley show varied land covers and also has very different population densities. This paper attempts to model the land use changes in the Barak valley over a period of 1988 to 2005, using an agent-based model. Each district is modelled as an agent of change here to capture the interactions between the various land uses and to decide on the resource allocation over the district to be allotted to shifting cultivation. The model accounts for both economic as well as geographic factors, like access to infrastructure while making the land use decision on a year-on-year basis. The model considers the need for shifting cultivation area for both a staple crop - rice and a non-staple crop based on the demand against the supply estimated from irrigated and rain-fed crop regions. While the model has been tuned based on the data till 1997, the simulated model outcome of 2005 is evaluated against an existing remote sensing derived land use map of the region and it is found to achieve an accuracy of about 85%. While the aggregated results across the region show good concurrence, the randomness inherent in the choice of the shifting cultivation land limits the ability to predict the precise locations. The paper will discuss the challenges including scale issues in model application for a region as diverse as Barak.

Abstract

The present work is a multi-temporal satellite based spatial dynamic study of an important coastal habitat, the Pichavaram mangrove ecosystem, over a period of 15 years. The study discusses the importance and the status of mangroves on both global and regional scales. Maximum likelihood estimate method of supervised classification technique has been used to classify the land use-land cover changes in the Pichavaram Reserve Forest, Killai Reserve Forest and Pichavaram Extension. The status of the classes has been monitored using Landsat ETM+ of 2000, 2006, 2011, and OLI of 2016 satellite imageries. The total area of the Pichavaram mangrove showed a net increase of approximately 11.41% of the total study area within a span of 15 years (2000–2016)

Abstract

pgRouting library provides functions to compute shortest path between any two points of a road network which is of great demand and also a topic of interest in the field of GIS, graph theory and transportation. To compute path in a road network, pgRouting functions process the entire road network which is a major bottleneck when it comes to routing in large road networks leading to the requirement of large server resources. A reduction/compression in the input network that is to be processed for path computation would improve the performance of pgRouting. In this study a map generalization based network model is proposed which extracts a significantly smaller subset of the road network aka skeleton which further used to divide the network into zones, that shall be selectively used in path computation. This results in processing a much smaller part of the network to compute path between any two points leading to an overall improvement in query performance of pgRouting when computing path, especially on large road networks. As part of assessment of this approach and its applicability to large road networks, the paper presents an in-depth analysis of the trade-offs between deviation in computed path and the performance gain in terms of space and time on road networks of varying sizes and topology to get a better understanding for both providing a sound proof of the utility of the proposed method and also to show its implementability within the current model of pgRouting or any other routing platforms.

Abstract

Many domain specific decisions of our day-to-day lives are dependent on weather. Weather and its variables have a significant impact on crop gro wth stages and crop producti vity.Weather forecast service has been started by In dia Meteorological Department (IMD) from the year 1945. To help the farming community, IMD has started pro viding district level agro-meteorological advisory based on the district-level Medium Range Forecast (MRF) f rom the year2008 fo r all districts through 130 agro-meteorological field units (AMFUs). Based on the MRF for five days, each AMFU is providing crop-s pecific agromet advice for the districts covered by that AMFU. To impro ve the effectiveness, IMD has started block level MRF on an experimental basis from the year 2015. Based on block level MRF, IMD is planning to provide block-level agromet advisory based on the block-level MRF. In such a situation, agromet scientisst haveto prepare agromet advisory for several blocks and crops at the earliest after receiving the block level MRF which will be disseminated to the farmers and stakeholders. In this context, research efforts have to be made to develop efficient system to enable agromet scientists to prepare the block-level agromet advice based on the block-level MRF. Normally, it can be observed that the weather and cro ps of nearby blocks may not vary significantly. S o, there is an opportunity to reuse the agromet advice prepared for one block to the nearby blocks. In this paper, we have pro posed an IT- based framewo rk to enable the agromet scientists to prepare the block level agromet advisoryby exploitingthe fact that the agromet advice which has been prepared for one block can be reused for another block if the weather condition and the crop is the same. The proposed framework pro vides the scope to exploit the reuse of agromet advisory prepared for one block toother blocks and could improve the efficiency of agromet scientists for the preparation of block-level agromet advisory based on the MRF