Phase preserving Fourier descriptor for shape-based image retrieval

Sokic, Emir; Konjicija, Samim

doi:10.1016/j.image.2015.11.002

Cited by 30 publications

(22 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But during acquiring the invariant properties, some valuable information have been lost. Hence another shape based image retrieval scheme has been suggested by Emir et al [7] [8] which overcomes the problem of existing scheme [6] and it also preserves the invariance properties of image. They have adopted only the phase of Fourier coefficients and it has been used for the specific points (or pseudomirror points) as a shape orientation reference.…”

Section: A Shape Visual Feature Descriptorsmentioning

confidence: 99%

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

et al. 2020

View full text Add to dashboard Cite

In this paper, a three stage hierarchical image retrieval scheme using a color, texture and shape visual contents (or descriptors) is proposed, since single visual content is not produce an adequate retrieval results effectively. This scheme has reduced the searching space during the image retrieval process at a certain extent due to the hierarchical mode. In Initial stage, the shape feature descriptor has been computed by simple fusion of histograms of gradients and invariant moments of segmented image planes. The shape based retrieval process has reduced the search space by discarding the non-relevant images from the universal dataset (or original dataset) effectively and kept the retrieved images into the intermediate dataset. In the second stage, the texture feature descriptors have been computed from the intermediate sub-image dataset by applying the adaptive tetrolet transform on image plane of preprocessed HSV color image. This transform provides the multi-resolution images with finer details by employing the tetrominoes and the proper arrangement of tetrominoes contributes the effective local geometry of image plane. The gray level co-occurrence matrix based texture feature descriptor is obtained by computing second order statistical parameters from each decomposed sub-image. At this stage, the most of the irrelevant images are discarded by retrieving the images from intermediate dataset but still some undesired images are left, those will be handled at the last stage. At this stage, fused color information is captured by applying the color autocorrelogram on both the non-uniform quantized color components of the preprocessed HSV color image. Finally, the color feature descriptor produces the desired retrieval results by discarding the irrelevant images from the texture based sub-image dataset. The proposed scheme has also low computational overhead due to the use of three descriptors at different stages separately. The retrieved results show the better accuracy as compared to the other related visual contents based image retrieval schemes.

show abstract

Section: A Shape Visual Feature Descriptorsmentioning

confidence: 99%

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

et al. 2020

View full text Add to dashboard Cite

show abstract

“…One of the most frequently used shape signatures is the centroid contour distance (CCD) according to [27,28,29,30]. The studies in [31,32,33], calculated centroid contour distance plot and angle code histogram (ACH) for leaf representation. The authors in [34] represented the leaf shape using centroid contour distance curve and proposed an ontology-based integrated approach for classification.…”

Section: A) Image Shape Signaturesmentioning

confidence: 99%

Leaf identification using radial basis function neural networks and SSA based support vector machine

Ahmed

Hussein

2020

PLoS ONE

View full text Add to dashboard Cite

In this research, an efficient scheme to identify leaf types is proposed. In that scheme, the leaf boundary points are fitted in a continuous contour using Radial Basis Function Neural Networks (RBFNN) to calculate the centroid of the leaf shape. Afterwards, the distances between predetermined points and the centroid were computed and normalized. In addition, the time complexity of the features' extraction algorithm was calculated. The merit of this scheme is objects' independence to translation, rotation and scaling. Moreover, different classification techniques were evaluated against the leaf shape features. Those techniques included two of the most commonly used classification methods; RBFNN and SVM that were evaluated and compared with other researches that used complex features extraction algorithms with much higher dimensionality. Furthermore, a third classification method with an optimization technique for the SVM using Salp Swarm Algorithm (SSA) was utilized showing a significant improvement over RBFNN and SVM.

show abstract

“…Image features are analyzed through the calculation of structural primitives and the technique of placement within the image because that technique is very effective against complex image structures. While the other technique method of calculating similar structure of images is based on statistics that include Fourier Power Spectrum Statistics [27], Co-Occurrence Matrices [28], [29], Fixed-Invariant Principal Component Analysis (SPCA) [19], [30], Tamura Feature [31], Wold Decomposition [32], Random Markov Fields [33], Fractal Models [34], and Multi-Resolution Filtering Techniques such as Gabor and Wavelet Transform [11]. The features found in texture techniques are statistically performed through the distribution of image intensity [11], [29], [31], [26], [35], [36].…”

Section: B Image Features Extractionmentioning

confidence: 99%

Visualising Image Data through Image Retrieval Concept using a Hybrid Technique: Songket Motif’s

Yusof¹,

Ismail²,

Majid³

2019

IJACSA

View full text Add to dashboard Cite

It has been proven that the massive dataset is strictly complex in Content Based image Retrieval (CBIR) because the present strategies in CBIR might have faced difficulties in feature extraction of the images. Moreover, technological constraints encountered in the analysis and extraction of the image arrays are how the system customizes the primitive geometric structures known as polygonal approximations structure. Hence, this study has discovered that image feature extraction is utilized by applying the Principal Component Analysis (PCA) technique, which is primarily based on the matrix of image representation that will enlarge the similarity of detection. The PCA approach needs to be enhanced resulting from the lack of the extraction of features in songket motives images. Therefore, this study proposes a new hybrid model that will integrate PCA with geometric techniques for image feature extraction to increase the recall and precision result. This paper employs the use of a qualitative experimental design model that involves three phases of activities. First, the analysis and design phase, secondly is a development phase, and lastly is the testing and evaluation phase. This paper focuses on those two phases in terms of design and development phases. The outcome process of the empirical phase is followed by designing the algorithm and model based on the result of literature review. This study has found that the hybrid between the principal component analysis model and the geometry technique will help to reduce the problems faced by the basic engineering technique model, which is the constraint in analysing and extracting the image features to customize the geometric primitive structure.

show abstract

Phase preserving Fourier descriptor for shape-based image retrieval

Cited by 30 publications

References 42 publications

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

Image Retrieval Scheme Using Quantized Bins of Color Image Components and Adaptive Tetrolet Transform

Leaf identification using radial basis function neural networks and SSA based support vector machine

Visualising Image Data through Image Retrieval Concept using a Hybrid Technique: Songket Motif’s

Contact Info

Product

Resources

About