Computer Sciencehttps://dyuthi.cusat.ac.in:443/xmlui/handle/purl/4392024-02-07T17:07:15Z2024-02-07T17:07:15ZState-of-the-Art: Transformation Invariant DescriptorsSreeraj, MAsha, Shttps://dyuthi.cusat.ac.in:443/xmlui/handle/purl/43192014-07-30T20:30:32Z2013-09-01T00:00:00ZState-of-the-Art: Transformation Invariant Descriptors
Sreeraj, M; Asha, S
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.
International Journal of Scientific & Engineering Research, Volume 4, Issue 9, september 2013
2013-09-01T00:00:00ZThe Effect of SIFT Features as Content Descriptors in the Context of Automatic Writer Identification in Malayalam LanguageSreeraj, MSumam, Mary Idiculahttps://dyuthi.cusat.ac.in:443/xmlui/handle/purl/43182014-07-30T20:30:08Z2012-12-07T00:00:00ZThe Effect of SIFT Features as Content Descriptors in the Context of Automatic Writer Identification in Malayalam Language
Sreeraj, M; Sumam, Mary Idicula
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
India Conference (INDICON), 2012 Annual IEEE
2012-12-07T00:00:00ZAutomatic Image Annotation Using SURF DescriptorsSreeraj, MMuhammed Anees, VSanthosh Kumar, Ghttps://dyuthi.cusat.ac.in:443/xmlui/handle/purl/43172014-07-30T20:30:29Z2012-12-07T00:00:00ZAutomatic Image Annotation Using SURF Descriptors
Sreeraj, M; Muhammed Anees, V; Santhosh Kumar, G
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.
India Conference (INDICON), 2012 Annual IEEE
2012-12-07T00:00:00ZContent Based Video Retrieval using SURF DescriptorSreeraj, MAsha, Shttps://dyuthi.cusat.ac.in:443/xmlui/handle/purl/43162014-07-30T20:30:28Z2013-08-29T00:00:00ZContent Based Video Retrieval using SURF Descriptor
Sreeraj, M; Asha, S
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.
2013 Third International Conference on Advances in Computing and Communications
2013-08-29T00:00:00Z