Object Recogniton Based on Undecimated Wavelet Transform
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This document presents a method for object recognition using undecimated wavelet transform (UWT) and k-nearest neighbor (k-NN) classifier, achieving up to 95.5% classification accuracy on the Columbia Object Image Library dataset (COIL-100). The approach effectively extracts features from images while enhancing performance by addressing the limitations of conventional discrete wavelet transform (DWT). The proposed system aims to improve object recognition efficiency, demonstrating high sensitivity and specificity in its results.
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![Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 07, Issue: 01, March 2018, Page No.1-3
ISSN: 2278-2397
1
Object Recogniton Based on Undecimated
Wavelet Transform
R.Umagowri1
, N.Soundararajan2
, B.Sakthisree3
1,2,3,
Assistant Professor, Department of Computer Science and Engineering, Mahendra Engineering College,
Mahendhirapuri, Namakkal District, Mallasamudram, Tamilnadu, India.
Abstract - Object Recognition (OR) is the mission of finding a specified object in an image or video sequence
in computer vision. An efficient method for recognizing object in an image based on Undecimated Wavelet
Transform (UWT) is proposed. In this system, the undecimated coefficients are used as features to recognize the
objects. The given original image is decomposed by using the UWT. All coefficients are taken as features for
the classification process. This method is applied to all the training images and the extracted features of
unknown object are used as an input to the K-Nearest Neighbor (K-NN) classifier to recognize the object. The
assessment of the system is agreed on using Columbia Object Image Library Dataset (COIL-100) database.
Key words: Object recognition, wavelet transform, K-Nearest Neighbor.
I. INTRODUCTION
Large amount of objects in images are recognized by human with small try, in spite of the fact that the image of
the objects may vary by different sizes / scale, diverse viewpoints or even when they are translated or rotated.
This assignment is still a test for computer vision systems. Over the years, extensive researches have been done
for the recognition of objects in an image. In this section, the review of literature is given for OR. A rotation
and validation invariant OR is explained in [1]. By utilizing local adaptive binarization and DoG channel, a
binary image reserving spotless object boundaries is accomplished. OR is done by using neural network.
OR based on tree-based context model is explained in [2]. This pattern incorporates worldwide image features,
dependencies between categories of object, and outputs of nearby detectors into one probabilistic structure. A
method for OR by developing the popular bag-of-words methods from the following two aspects is presented in
[3]. To make the semantic significant object parts, a fast method is approached by exploiting the geometric site
allocation of the nearby salient regions. At last, multi-kernel learning framework is utilized to add extracted
features. OR using Bayesian approach based on Gaussian process regression is discussed in [4]. To study the
likelihood of image features, gaussian process regression is given. The selection of suitable camera parameters
is formulated as a chronological optimization problem. A video surveillance OR algorithm is discussed in [5], in
which improved invariant moments and length-width ratio of object are extracted as shape feature, while color
histograms of object are used as color feature. Closed 2D curves are parameterized, and Fourier descriptors are
utilized in [6] to create a set of normalized coefficients which are invariant under affine transformations. The
method is recognized on silhouettes of aircraft.
Online kernel dictionary learning for OR is explained in [7]. An optimization model to concurrently execute
prototype selection and kernel dictionary learning is approached. A row-sparsity regularization term on the
representation matrix is introduced to make sure that only a few samples are used to recreate the dictionary. A
method using hidden Markov models is described capable of dealing with severe part occlusions in different OR
situations [8]. A hidden Markov model is developed for each probable class in an ensemble trained with
database from each class example. Introduced the strategy for search space sorting and stopping criterion. In a
multifaceted environment, simultaneous OR and tracking has been one of the challenging topics in computer
vision and robotics [9]. The data-driven unfalsified control is discussed for solving this problem in visual
servoing. It recognizes a target through matching image features with a 3-D model and then tracks them through
dynamic visual servoing. The discussion in [10] is about an image as a local descriptor tensor and use a
Multilinear Supervised Neighborhood Embedding (MSNE) for discriminant feature extraction. It includes: 1) a](https://image.slidesharecdn.com/iirijcoa201835-190415064018/85/Object-Recogniton-Based-on-Undecimated-Wavelet-Transform-1-320.jpg)
![Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 07, Issue: 01, March 2018, Page No.1-3
ISSN: 2278-2397
3
Table 1: Performance of the UWT based object recognition system
UWT
Resolution
Level
Accuracy
(%)
Sensitivity
(%)
Specificity
(%)
1 89.5 89 90
2 92 91 93
3 95.5 95 96
4 90.5 90 91
5 88.5 88 89
It is observed from the table 1 that the UWT based OR system provides 95.5% accuracy by using the features at
3rd
level UWT decomposition and using training samples acquired.
IV. CONCLUSION
In this paper, an approach for OR based on UWT features is presented. Undecimated coefficients are used for
feature extraction. Obtained features are applied to the classification stage. KNN classifier uses the trained
images from the feature extraction stage as references and classifies the test objects. The classification
performance of the UWT based system is evaluated by using classification rate in percentage. Experimental
results show that the proposed approach produces 95.5% accuracy.
References
[1] Kim, Kyekyung , Kim, Joongbae ; Kang, Sangseung ; Kim, Jaehong ; Lee, Jaeyeon , “Object recognition
for cell manufacturing system”,biquitous Robots and Ambient Intelligence (URAI), 2012, pp 512 – 514.
[2] Choi, Myung Jin , Torralba, Antonio B. ; Willsky, Alan S., “A Tree-Based Context Model for Object
Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 34 , Issue: 2,
pp.240 – 252.
[3] Wang, Mei, Wu, Yanling , Li, Guangda ,Zhou, Xiang-Dong, “Object Recognition via Adaptive Multi-level
Feature Integration”, 12th International Asia-Pacific on Web Conference (APWEB), 2010, pp. 253 – 259.
[4] Huber, Marco F., Dencker, Tobias, Roschani, Masoud ,Beyerer, Jürgen, “Bayesian active object recognition
via Gaussian process regression”, Information Fusion (FUSION), 2012, pp.1718 – 1725.
[5] Wu, Jun , Xiao, Zhi-Tao, “Video surveillance object recognition based on shape and color features”, Image
and Signal Processing (CISP), 2010, pp.451 – 454.
[6] Arbter K., Snyder W. E., Burkhardt H., and Hirzinger G., “Application of affine-invariant Fourier
descriptors to recognition of 3-d objects.” IEEE Trans. Pattern Anal. Mach. Intell., vol.12, no. 7, pp. 640–
647, 1990.
[7] Liu, H., and Sun, F., (2016), "Online Kernel Dictionary Learning for Object Recognition", IEEE
International Conference on Automation Science and Engineering, 268-273.
[8] Guerrero-Peña, F. A., and Vasconcelos, G. C., (2016), "Search-Space Sorting with Hidden Markov Models
for Occluded Object Recognition", IEEE 8th International Conference on Intelligent Systems (IS), 47- 52.
[9] Jiang, P., et al., (2016), "Unfalsified Visual Servoing for Simultaneous Object Recognition and Pose
Tracking", IEEE Transactions on Cybernetics, 46(12): 3032-3046.
[10] Han, X. H., et al., (2012), "Multilinear Supervised Neighborhood Embedding of a Local Descriptor Tensor
for Scene/Object Recognition", IEEE Transactions on Image Processing, 21(3):1314-1326.](https://image.slidesharecdn.com/iirijcoa201835-190415064018/85/Object-Recogniton-Based-on-Undecimated-Wavelet-Transform-3-320.jpg)
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- 2. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 07, Issue: 01, March 2018, Page No.1-3 ISSN: 2278-2397 2 feature extraction approach denoted as the histogram of orientation weighted with a normalized histogram of gradients for local region representation. 2) an image representation framework denoted as the local descriptor tensor and 3) an MSNE analysis algorithm. II. PROPOSED METHODOLOGY The proposed system for the recognition using coil database is built based on UWT and K-NN classifier for classification. The main aim of the proposed system is to recognize object in an image efficiently. It is composed of two computational blocks; feature or information extraction, and classification. Figure 1 shows the flow of the OR system. Figure 1: Proposed Object Recognition System Development to DWT is the UWT in order to ease the major disadvantage i.e. lack of translation invariance of DWT. By taking the up and down samples in the DWT only, it was achieved. So, the UWT result contains the amount of coefficients in same as the amount of pixels in the input image at each step which entail more space for additional process and feature extraction might be complicated. The high dimensionality UWT coefficients space is decreased to avoid by fusion approach and the predefined number dominant features are selected for improved classification. III. RESULTS AND DISCUSSIONS In this section, the performance of the proposed system based on the undecimated features is discussed and verified. Features from UWT are given as input to the classification stage. K-NN is used as classifier to classify the images from the trained images to recognize the object. The classification accuracy is used to analyze the performance of the system. Figure 2 shows the sample objects in the COIL database. It consists of 100 objects of 128 x128 pixels resolution. Table 1 shows the accuracy obtained by the system. Figure 2: COIL Database
- 3. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 07, Issue: 01, March 2018, Page No.1-3 ISSN: 2278-2397 3 Table 1: Performance of the UWT based object recognition system UWT Resolution Level Accuracy (%) Sensitivity (%) Specificity (%) 1 89.5 89 90 2 92 91 93 3 95.5 95 96 4 90.5 90 91 5 88.5 88 89 It is observed from the table 1 that the UWT based OR system provides 95.5% accuracy by using the features at 3rd level UWT decomposition and using training samples acquired. IV. CONCLUSION In this paper, an approach for OR based on UWT features is presented. Undecimated coefficients are used for feature extraction. Obtained features are applied to the classification stage. KNN classifier uses the trained images from the feature extraction stage as references and classifies the test objects. The classification performance of the UWT based system is evaluated by using classification rate in percentage. Experimental results show that the proposed approach produces 95.5% accuracy. References [1] Kim, Kyekyung , Kim, Joongbae ; Kang, Sangseung ; Kim, Jaehong ; Lee, Jaeyeon , “Object recognition for cell manufacturing system”,biquitous Robots and Ambient Intelligence (URAI), 2012, pp 512 – 514. [2] Choi, Myung Jin , Torralba, Antonio B. ; Willsky, Alan S., “A Tree-Based Context Model for Object Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume: 34 , Issue: 2, pp.240 – 252. [3] Wang, Mei, Wu, Yanling , Li, Guangda ,Zhou, Xiang-Dong, “Object Recognition via Adaptive Multi-level Feature Integration”, 12th International Asia-Pacific on Web Conference (APWEB), 2010, pp. 253 – 259. [4] Huber, Marco F., Dencker, Tobias, Roschani, Masoud ,Beyerer, Jürgen, “Bayesian active object recognition via Gaussian process regression”, Information Fusion (FUSION), 2012, pp.1718 – 1725. [5] Wu, Jun , Xiao, Zhi-Tao, “Video surveillance object recognition based on shape and color features”, Image and Signal Processing (CISP), 2010, pp.451 – 454. [6] Arbter K., Snyder W. E., Burkhardt H., and Hirzinger G., “Application of affine-invariant Fourier descriptors to recognition of 3-d objects.” IEEE Trans. Pattern Anal. Mach. Intell., vol.12, no. 7, pp. 640– 647, 1990. [7] Liu, H., and Sun, F., (2016), "Online Kernel Dictionary Learning for Object Recognition", IEEE International Conference on Automation Science and Engineering, 268-273. [8] Guerrero-Peña, F. A., and Vasconcelos, G. C., (2016), "Search-Space Sorting with Hidden Markov Models for Occluded Object Recognition", IEEE 8th International Conference on Intelligent Systems (IS), 47- 52. [9] Jiang, P., et al., (2016), "Unfalsified Visual Servoing for Simultaneous Object Recognition and Pose Tracking", IEEE Transactions on Cybernetics, 46(12): 3032-3046. [10] Han, X. H., et al., (2012), "Multilinear Supervised Neighborhood Embedding of a Local Descriptor Tensor for Scene/Object Recognition", IEEE Transactions on Image Processing, 21(3):1314-1326.