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Mohamed Sathik, M.
- Multilevel Approach of CBIR Techniques for Vegetable Classification Using Hybrid Image Features
Abstract Views :177 |
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Authors
Affiliations
1 Department of Computer Science, Nesamony Memorial Christian College, IN
2 Sadakathullah Appa College, IN
1 Department of Computer Science, Nesamony Memorial Christian College, IN
2 Sadakathullah Appa College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 6, No 3 (2016), Pagination: 1174-1179Abstract
CBIR is a technique to retrieve images semantically relevant to query image from an image database. The challenge in CBIR is to develop a method that should increase the retrieval accuracy and reduce the retrieval time. In order to improve the retrieval accuracy and runtime, a multilevel CBIR approach is proposed in this paper. In the first level, the color attributes like mean and standard deviations are proposed to calculate on HSV color space to retrieve the images with minimum disparity distance from the database. In order to minimize search area, in the second level Local Ternary Pattern is proposed on images which were selected from the first level. Experimental results and comparisons demonstrate the superiority of the proposed approach.Keywords
Content Based Image Retrieval (CBIR), Gray Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP), Local Ternary Pattern (LTP).
- Hybrid Compression of Cervical Images by Segmenting Nuclei-Cytoplasm
Abstract Views :161 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Nesamony Memorial Christian College, IN
2 Sadakathullah Appa College, IN
3 Department of Statistics, Manonmaniam Sundaranar University, IN
1 Department of Computer Science, Nesamony Memorial Christian College, IN
2 Sadakathullah Appa College, IN
3 Department of Statistics, Manonmaniam Sundaranar University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 3, No 2 (2012), Pagination: 522-525Abstract
A hybrid image compression method is proposed by which the Nuclei-Cytoplasm of the image is completely restorable and the background part of the image is restorable with insignificant loss. In Hybrid Compression of Cervical Images by Segmenting Nuclei-Cytoplasm, the image is subjected to binary segmentation to detect Background and Nuclei-Cytoplasm. The image is compressed by standard lossy compression method. The difference between the lossy image and the original image is computed as residue. The residue at the Nuclei-Cytoplasm area is compressed by standard lossless compression method by which the Nuclei-Cytoplasm area is completely restorable. This method gives a low bit rate than the lossless compression methods.Keywords
Edge Detection, Segmentation, Image Compression.- Face Recognition Based on Local Derivative Tetra Pattern
Abstract Views :288 |
PDF Views:5
Authors
Affiliations
1 Department of Computer Applications, Nesamony Memorial Christian College, IN
2 Department of Computer Science, Sadakathullah Appa College, IN
3 Department of Computer Science, Nesamony Memorial Christian College, IN
1 Department of Computer Applications, Nesamony Memorial Christian College, IN
2 Department of Computer Science, Sadakathullah Appa College, IN
3 Department of Computer Science, Nesamony Memorial Christian College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 7, No 3 (2017), Pagination: 1393-1400Abstract
This paper proposes a new face recognition algorithm called local derivative tetra pattern (LDTrP). The new technique LDTrP is used to alleviate the face recognition rate under real-time challenges. Local derivative pattern (LDP) is a directional feature extraction method to encode directional pattern features based on local derivative variations. The nth -order LDP is proposed to encode the first (n-1)th order local derivative direction variations. The LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. The local tetra pattern (LTrP) encodes the relationship between the reference pixel and its neighbours by using the first-order derivatives in vertical and horizontal directions. LTrP extracts values which are based on the distribution of edges which are coded using four directions. The LDTrP combines the higher order directional feature from both LDP and LTrP. Experimental results on ORL and JAFFE database show that the performance of LDTrP is consistently better than LBP, LTP and LDP for face identification under various conditions. The performance of the proposed method is measured in terms of recognition rate.Keywords
Local Binary Pattern (LBP), Local Ternary Pattern (LTP), Local Derivative Pattern (LDP), Local Tetra Pattern (LTrP).References
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