{"title":"Video Retrieval based on Patterns of Oriented Edge Magnitude","authors":"K. R. Holla, B. H. Shekar","doi":"10.1145/2983402.2983433","DOIUrl":"https://doi.org/10.1145/2983402.2983433","url":null,"abstract":"In this work, a video retrieval system is proposed based on POEM (Patterns of Oriented Edge Magnitudes) descriptor. In the first stage, the input video is partitioned into shots based on Gabor moments and keyframes are selected from each shot based on Temporally Maximum Occurrence Frame (TMOF). In the next stage, the POEM descriptor is computed from each keyframe for robust image/frame representation. Given a query frame, the descriptor is obtained from it in a similar manner, and this descriptor is compared with the descriptors of the video keyframes using nearest neighbour matching technique to find the matching keyframe. We have conducted experiments on the TRECVID video segments to exhibit the superiority of the proposed approach for video retrieval applications.","PeriodicalId":283626,"journal":{"name":"Proceedings of the Third International Symposium on Computer Vision and the Internet","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123929169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Progressive Image Denoising using Fast Noise Variance Estimation","authors":"B. K. Thote, K. Jondhale","doi":"10.1145/2983402.2983440","DOIUrl":"https://doi.org/10.1145/2983402.2983440","url":null,"abstract":"The patch-less Progressive Image Denoising(PID) is physical process of reducing the noise in image based on deterministic annealing i.e. temperature decreases from high to low so that shape of kernel changes according to it. The results of PID implementation are good and excellent for both natural and computer generated images i.e. artificial or synthetic images. It estimate the noise using robust noise estimation. PID algorithm is only for denoising additive white Gaussian noise(awgn). For using PID the requirement is original image (noise free image) and amount of noise added to it. In real scenario, it is not possible to get the knowledge of noise level available in any image. This paper gives an approach to automatically estimate the noise level in the given input image and then denoise the image using PID. Experimental results demonstrate that proposed algorithm outperforms both objective and subjective fidelity criteria in image denoising.","PeriodicalId":283626,"journal":{"name":"Proceedings of the Third International Symposium on Computer Vision and the Internet","volume":"424 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133716702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Deep Residual Networks with Exponential Linear Unit","authors":"Anish Shah, Eashan Kadam, Hena Shah, Sameer Shinde","doi":"10.1145/2983402.2983406","DOIUrl":"https://doi.org/10.1145/2983402.2983406","url":null,"abstract":"The depth of convolutional neural networks is a crucial ingredient for reduction in test errors on benchmarks like ImageNet and COCO. However, training a neural network becomes difficult with increasing depth. Problems like vanishing gradient and diminishing feature reuse are quite trivial in very deep convolutional neural networks. The notable recent contributions towards solving these problems and simplifying the training of very deep models are Residual and Highway Networks. These networks allow earlier representations (from the input or those learned in earlier layers) to flow unimpededly to later layers through skip connections. Such very deep models with hundreds or more layers have lead to a considerable decrease in test errors, on benchmarks like ImageNet and COCO. In this paper, we propose to replace the combination of ReLU and Batch Normalization with Exponential Linear Unit (ELU) in Residual Networks. Our experiments show that this not only speeds up the learning behavior in Residual Networks, but also improves the classification performance as the depth increases. Our model increases the accuracy on datasets like CIFAR-10 and CIFAR-100 by a significant margin.","PeriodicalId":283626,"journal":{"name":"Proceedings of the Third International Symposium on Computer Vision and the Internet","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127830315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the Third International Symposium on Computer Vision and the Internet","authors":"","doi":"10.1145/2983402","DOIUrl":"https://doi.org/10.1145/2983402","url":null,"abstract":"","PeriodicalId":283626,"journal":{"name":"Proceedings of the Third International Symposium on Computer Vision and the Internet","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133927779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}