A modified Gabor function for content based image retrieval

Sastry, C. S.; Ravindranath, M.; Pujari, Arun K.; Deekshatulu, B. L.

doi:10.1016/j.patrec.2006.07.015

Cited by 34 publications

(21 citation statements)

References 11 publications

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“…We report the results of empirical evaluation of our method and compare it with six state-of-the-art matching algorithms on three standard datasets: the Smithsonian isolated leaf dataset [32], Kimia's object dataset [31] and Brodatz texture dataset [44]. We compare the performance of our method with a modified Gabor-based approach [21], a local binary pattern (LBP)-based approach [35], and three recently proposed featurebased approaches [30], [32], [33]. We refer to the indexing and retrieval method presented in [33] as the BAC 3 method.…”

Section: Resultsmentioning

confidence: 99%

“…We evaluate the performance of various methods using precision-recall curves, average retrieval performance [21], [29] and recognition rates. Recall and precision are defined as Precision = Number of relevant images retrieved Total number of images retrieved ,…”

Section: Resultsmentioning

confidence: 99%

“…For the sub-dataset containing rotated images only, on average RSICD obtained 7.9352 out of 8 retrieved images per shape. Whereas the BAC [33], IDSC+DP [32], SC [30], DC [15], LBP [35] and Gabor-based methods [21] In our CBIR experiments, to determine the class label of a given test image, we find its nth nearest neighbor among the training images, and then assign that training image's estimated class label (given by our RSICD algorithm) to the test image. The nth rank recognition rate is therefore defined as the ratio of the number of test images' nth nearest neighbors (among the training images) that are assigned with the same class labels as the true labels of the test images, to the total number of test images.…”

Section: ) Results On the Smithsonian Dataset With 18 Classesmentioning

confidence: 99%

“…In the experiment section, we introduce both rotation and scale variations to shape-based databases (Smithsonian isolated leaf and Kimia) and compare RSICD with four other state-of-the-art methods (LBP [35], BAC [33], IDSC+DP [32] and SC [30]). We further demonstrate the effectiveness of RSICD on a texture-based database (Brodatz) by comparing it with three other texture-based methods (Modified Gabor [21], DC [15] and LBP [35]). The organization of the paper is as follows.…”

mentioning

confidence: 99%

“…However, the chosen directions of Gabor filters may not correspond to the orientation of the content in the query image. Hence, a feature extraction method that is independent of orientation and scale in the image is desirable [21]. Wavelet-based methods have been proposed to achieve rotation invariance for image classification and retrieval [22], [23].…”

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confidence: 99%

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In-Plane Rotation and Scale Invariant Clustering Using Dictionaries

Chen

Sastry

Patel

et al. 2013

IEEE Trans. on Image Process.

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Abstract-In this paper, we present an approach that simultaneously clusters images and learns dictionaries from the clusters. The method learns dictionaries and clusters images in the radon transform domain. The main feature of the proposed approach is that it provides both in-plane rotation and scale invariant clustering, which is useful in numerous applications, including content-based image retrieval (CBIR). We demonstrate the effectiveness of our rotation and scale invariant clustering method on a series of CBIR experiments. Experiments are performed on the Smithsonian isolated leaf, Kimia shape, and Brodatz texture datasets. Our method provides both good retrieval performance and greater robustness compared to standard Gabor-based and three state-of-the-art shape-based methods that have similar objectives.

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