| Abstract: | As the popularity of digital videos increases, a large number illegal videos are being generated and getting published. Video copies are generated by performing various sorts of transformations on the original video data. For effectively identifying such illegal videos, the image features that are invariant to various transformations must be extracted for performing similarity matching. An image feature can be its local feature or global feature. Among them, local features are powerful and have been applied in a wide variety of computer vision aplications .This paper focuses on various recently proposed local detectors and descriptors that are invariant to a number of image transformations. |
| Description: | International Journal of Scientific & Engineering Research, Volume 4, Issue 9, september 2013 |
| URI: | http://dyuthi.cusat.ac.in/purl/4319 |
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| State-of-the-Ar ... Invariant Descriptors.pdf | (1.365Mb) |
| Abstract: | The span of writer identification extends to broad domes like digital rights administration, forensic expert decisionmaking systems, and document analysis systems and so on. As the success rate of a writer identification scheme is highly dependent on the features extracted from the documents, the phase of feature extraction and therefore selection is highly significant for writer identification schemes. In this paper, the writer identification in Malayalam language is sought for by utilizing feature extraction technique such as Scale Invariant Features Transform (SIFT).The schemes are tested on a test bed of 280 writers and performance evaluated |
| Description: | India Conference (INDICON), 2012 Annual IEEE |
| URI: | http://dyuthi.cusat.ac.in/purl/4318 |
| Files | Size |
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| The Effect of S ... in Malayalam Language.pdf | (833.4Kb) |
| Abstract: | In recent years there is an apparent shift in research from content based image retrieval (CBIR) to automatic image annotation in order to bridge the gap between low level features and high level semantics of images. Automatic Image Annotation (AIA) techniques facilitate extraction of high level semantic concepts from images by machine learning techniques. Many AIA techniques use feature analysis as the first step to identify the objects in the image. However, the high dimensional image features make the performance of the system worse. This paper describes and evaluates an automatic image annotation framework which uses SURF descriptors to select right number of features and right features for annotation. The proposed framework uses a hybrid approach in which k-means clustering is used in the training phase and fuzzy K-NN classification in the annotation phase. The performance of the system is evaluated using standard metrics. |
| Description: | India Conference (INDICON), 2012 Annual IEEE |
| URI: | http://dyuthi.cusat.ac.in/purl/4317 |
| Files | Size |
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| Automatic Image ... Using SURF Descriptors.pdf | (714.0Kb) |
| Abstract: | This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor. |
| Description: | 2013 Third International Conference on Advances in Computing and Communications |
| URI: | http://dyuthi.cusat.ac.in/purl/4316 |
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| Content Based V ... using SURF Descriptor.pdf | (334.0Kb) |
| Abstract: | Detection of Objects in Video is a highly demanding area of research. The Background Subtraction Algorithms can yield better results in Foreground Object Detection. This work presents a Hybrid CodeBook based Background Subtraction to extract the foreground ROI from the background. Codebooks are used to store compressed information by demanding lesser memory usage and high speedy processing. This Hybrid method which uses Block-Based and Pixel-Based Codebooks provide efficient detection results; the high speed processing capability of block based background subtraction as well as high Precision Rate of pixel based background subtraction are exploited to yield an efficient Background Subtraction System. The Block stage produces a coarse foreground area, which is then refined by the Pixel stage. The system’s performance is evaluated with different block sizes and with different block descriptors like 2D-DCT, FFT etc. The Experimental analysis based on statistical measurements yields precision, recall, similarity and F measure of the hybrid system as 88.74%, 91.09%, 81.66% and 89.90% respectively, and thus proves the efficiency of the novel system. |
| Description: | Applications of Digital Information and Web Technologies (ICADIWT), 2014 Fifth International Conference on the |
| URI: | http://dyuthi.cusat.ac.in/purl/4315 |
| Files | Size |
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| Hybrid Backgrou ... i-level CodeBook Model.pdf | (248.8Kb) |
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