Shape retrieval based on manifold learning by fusion of dissimilarity measures

Chahooki, Mohammad Ali Zare; Charkari, Nasrollah Moghadam

doi:10.1049/iet-ipr.2010.0548

Cited by 22 publications

(11 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The manifold learning based shape retrieval using fusion of dissimilarity measures was developed by Chahooki et al [2]. The approach integrates contour-based (Centroid and Farthest corner point) and region-based (Squared and Zernike moments) shape retrieval methods.…”

Section: B Related Workmentioning

confidence: 99%

Compact centroid distance shape descriptor based on object area normalization

Arjun¹,

Mirnalinee²,

Tamilarasan³

2014

2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies

View full text Add to dashboard Cite

Shape descriptors are more powerful to discern objects present in the images. The present work is focused on simple contour based shape descriptor using centroid distance function and it works on closed contour objects. Object area normalization is performed to obtain 'N' normalized contour points. The centroid distance feature extraction is performed on all normalized points. It forms simple I-D feature vector of size 'N'. For similarity matching correlation coefficient metric is used. This shape descriptor satisfies affine invariance property.The proposed idea is tested on MPEG-7 CE Shape-l Part-B dataset images to validate its effectiveness. Experimental results shows that proposed compact centroid distance shape descriptor is more accurate than basic centroid distance shape descriptor and it saves space and time requirements at processing.

show abstract

Section: B Related Workmentioning

confidence: 99%

Compact centroid distance shape descriptor based on object area normalization

Arjun¹,

Mirnalinee²,

Tamilarasan³

2014

2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies

View full text Add to dashboard Cite

show abstract

“…Manifold learning is used in different machine vision applications such as CBIR [13], [14], [15], shape analysis [16], [17], face recognition [18], [19], [20], [21], facial expression recognition [22], [23], [24], tracking [25], [26], action recognition [27], [28], and pose estimation [29], [30]. A brief review of the manifold learning in machine vision applications are described below.…”

Section: Manifold Learning In Machine Visionmentioning

confidence: 99%

Improvement of Supervised Shape Retrieval by Learning the Manifold Space

Chahooki

Charkari

2011

2011 7th Iranian Conference on Machine Vision and Image Processing

View full text Add to dashboard Cite

Manifold learning is the technique that aims for finding a constructive way to embed the data from a highdimensional space into a low-dimensional manifold based on nonlinear approaches. In this paper a supervised manifold learning method for shape recognition is proposed. The approach is based on learning the manifold space for training samples and map the test samples to the learned space by a Generalised Regression Neural Network (GRNN). The main goal in this paper is to propose a new feature vector to coincide semantic and Euclidean distances. To accomplish this, the desired topological manifold was learnt by a global distance driven non-linear feature extraction method. The experiments showed that the geometrical distances between the test samples on the manifold space are more related to their semantic distance. To fuse the results of shape recognition based on contour and region based methods, in our framework the final result of shape recognition is based on committee decision in three manifold spaces. The experimental results confirmed the effectiveness and validity of the proposed method.

show abstract

“…The texture based methods [7,8,9] employ texture features including the gray level co-occurrence matrix, wavelet transform, Markov random field, local binary pattern, etc. The shape based techniques [10,11,12] adopt shape features including boundary chain code, Fourier descriptor, shape moments, etc. For the shape moments related methods, both Hu invariant moments and Zernike moments are used very popularly.…”

Section: Introductionmentioning

confidence: 99%

Contour Points based P2P Algorithm for Shape Matching and Image Retrieval

Zhao¹,

Ji²,

Sun³

et al. 2014

Appl. Math. Inf. Sci.

View full text Add to dashboard Cite

Based on the extracted contours of objects from images, one point to point (P2P) method is proposed for shape matching and image retrieval. Taking contour of one object as reference, contour of another object is transformed to reach their best match, during which the similarity is evaluated by comparison of two corresponding sets of contour points. Translation, scaling and rotation are all considered in transformation, thus the algorithm is robust to objects with different position, size and posture. The experimental results are presented and compared with those from two popular shape based techniques, Hu invariant moments and Zernike moments. Performance of our new approach has proved its efficiency in both matching accuracy and computational expense, and it can be used in related applications together with the other kinds of shape features or even color features, texture features, etc.

show abstract

Shape retrieval based on manifold learning by fusion of dissimilarity measures

Cited by 22 publications

References 51 publications

Compact centroid distance shape descriptor based on object area normalization

Compact centroid distance shape descriptor based on object area normalization

Improvement of Supervised Shape Retrieval by Learning the Manifold Space

Contour Points based P2P Algorithm for Shape Matching and Image Retrieval

Contact Info

Product

Resources

About