Localized Image Retrieval Based on Interest Points

Meng, Fei; Guo, Baolong; Wu, Xianxiang

doi:10.1016/j.proeng.2012.01.496

Cited by 5 publications

(7 citation statements)

References 13 publications

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“…100 images from each semantic group are selected. Using precision-recall [21] and retrieval accuracy graphs [3], the retrieval accuracies of the six feature selection methods are compared. The precision-recall graph and retrieval accuracy are shown in Figure 2 From the results shown in Figures 2 and 3, fuzzy rough feature selection method has better results than the other feature selection methods.…”

Section: -Experiments Resultsmentioning

confidence: 99%

Using fuzzy rough feature selection for image retrieval system

Lotfabadi

Shiratuddin

Wong

2013

2013 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)

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Abstract:Feature selection is an important step in processing the images especially for applications such as content based image retrieval. In large multimedia databases, it may not be practical to search through the entire database in order to retrieve similar images from a query. Good data structures for similarity search and indexing are needed, and the existing data structures do not scale well for the high dimensional multimedia descriptors. Thus feature selection is an important step. Fuzzy rough feature selection method has many advantages in determining the relevant features. In this paper, five feature selection methods are compared with the fuzzy rough method. These five feature selection methods are Relief-F, Information Gain, Gain Ratio, OneR and the statistical measure 2 . The main purpose of the comparison is to rank the image features and see which method provides better results. An image retrieval dataset (COREL dataset) was used in the comparison. In order to evaluate the performance of the six methods, ranking of the important features is defined. This is then used to compare with the automated ranking produced by the aforesaid feature selection methods. Results show that the retrieval system using fuzzy rough feature selection has better retrieval accuracy and provide good Precision Recall performance. The advantages of the use of fuzzy rough feature selection will also be discussed in the paper.

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Section: -Experiments Resultsmentioning

confidence: 99%

Using fuzzy rough feature selection for image retrieval system

Lotfabadi

Shiratuddin

Wong

2013

2013 IEEE Symposium on Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP)

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“…Interest points detection. Many interest point detection methods have been proposed in literatures, among them, Harris corner detector is the most reliable one [3]. It is translation and rotation invariant.…”

Section: Interest Points Matchingmentioning

confidence: 99%

“…The similarity is measured based on the local gray or color information of all interest points for general image retrieval methods [1][2][3]. Since the gray information has been used during the interest point matching process for the proposed algorithm, the spatial distribution of the interest points is used here to measure the similarity of two images.…”

Section: Similarity Measurement Based On Spatial Distributionmentioning

confidence: 99%

“…The description method based on interest point is robust to the geometric transformation of the image. It is found that most traditional image retrieval methods based on interest points [1][2][3] are based on similar strategies. For these methods, image retrieval is implemented based on the spatial distribution of interest points, or the gray or color information around interest points.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Image Retrieval Method Based on Local Features of Interest Points

Wang

Zeng

2011

AMM

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To measure the similarity between two images, the local information around all the points were usually used for traditional image retrieval methods based on interest points. The accuracy would be influenced by the dissimilar interest points in the background region or in the uninterested regions. A new image retrieval method based on interest points was proposed in this paper. The most similar interest points were chosen firstly based on feature matching techniques, then the similarity was measured based on the local gray information around the preserved similar interest points. Experiments show that the proposed method is more accurate than traditional image retrieval method based on interest points.

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“…One is that the histogram of circumscribed circle radius from adjacent edge to the centroid of feature points, another is the histogram of distances between feature points and the top left corner of the image. In [3] and [4], the authors use the convex hull of interest points to describe the image feature, combine SVM learning method and Zernike moments respectively to improve the efficiency of image retrieval, moreover, both methods use annular histogram to improve the performance of retrieval. Since the method based on annular histogram is calculation efficient, invariant to image rotation and translation, and it can indicate the spatial distribution of feature points to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

An Image Retrieval Method Based on Hu Invariant Moment and Improved Annular Histogram

Xiang¹,

Yong²,

Dandan³

et al. 2014

ElAEE

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Image retrieval methods based on annular histogram of feature points are calculation efficient, invariant to image rotation and translation transform. However, these methods have two main disadvantages. One is that the annular histogram can't describe the spatial distribution of feature points accurately, thus different images may have similar annular histogram. Another one is the methods based on feature points can not describe the shape information of object. These disadvantages affect the retrieval accuracy to a certain extent. In this paper, an image retrieval method based on Hu invariant moments and improved annular histogram is proposed. Firstly, edge of image is detected and feature points are calculated based on the edge curvature. Then, image features are described based on both the edge and points. Annular histogram combined with standard deviation ellipse method is used to describe the spatial distribution of feature points. Hu invariant moment of the edge is used to represent the object's shape information. Finally, the similarity is measured based on both the point feature and the shape feature. Experiment results show that the proposed method can improve the image retrieval precision effectively.

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Localized Image Retrieval Based on Interest Points

Cited by 5 publications

References 13 publications

Using fuzzy rough feature selection for image retrieval system

Using fuzzy rough feature selection for image retrieval system

An Image Retrieval Method Based on Local Features of Interest Points

An Image Retrieval Method Based on Hu Invariant Moment and Improved Annular Histogram

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