BIG-OH: BInarization of gradient orientation histograms

Baber, Junaid; Dailey, Matthew N.; Satoh, Shin’ichi; Afzulpurkar, Nitin; Bakhtyar, Maheen

doi:10.1016/j.imavis.2014.08.006

Cited by 20 publications

(25 citation statements)

References 36 publications

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“…However, these methods are limited to small or moderate databases. To make searching computationally effective, either the descriptors are quantized to Hamming space [9] or quantized to single image feature, aka BoVW [10], [24]- [26].…”

Section: Related Workmentioning

confidence: 99%

“…There are number of prominent techniques for quantization such as Fisher Vector [4], VLAD [5]- [8], binary quantizer [9], and BoVW model [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

“…BoVW model is widely used for several computer vision and image based applications such as image retrieval [9]- [11] and image classification [7]. The idea of BoVW model is inspired from text retrieval system where the text document is represented by the frequencies of words.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of SIFT and Convolutional Neural Network for Image Retrieval

Sachdeva¹,

Baber²,

Bakhtyar³

et al. 2017

ijacsa

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Abstract-Convolutional Neural Network (NN) has gained a lot of attention of the researchers due to its high accuracy in classification and feature learning. In this paper, we evaluated the performance of CNN used as feature for image retrieval with the gold standard feature, aka SIFT. Experiments are conducted on famous Oxford 5k data-set. The mAP of SIFT and CNN is 0.6279 and 0.5284, respectively. The performance of CNN is also compared with bag of visual word (BoVW) model. CNN achieves better accuracy than BoVW.

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Section: Related Workmentioning

confidence: 99%

“…There are number of prominent techniques for quantization such as Fisher Vector [4], VLAD [5]- [8], binary quantizer [9], and BoVW model [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of SIFT and Convolutional Neural Network for Image Retrieval

Sachdeva¹,

Baber²,

Bakhtyar³

et al. 2017

ijacsa

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“…The descriptor SIFT [8] is also one of the major reasons for local features popularity. Many CBCD and image retrieval systems have been proposed based on SIFT and other local features such as GLOH [17], CSLBP [18], SURF [9], and BIG-OH [19].…”

Section: A Content-based Image Copy Detectionmentioning

confidence: 99%

“…We also evaluate the performance of NBD descriptors on larger dataset (Oxford dataset). We follow same steps used in BIGOH [19]. We evaluate the retrieval performance of proposed descriptors by mean average precision (mAP).…”

Section: Experiments Ii: Image Retrievalmentioning

confidence: 99%

Performance Enhancement of Patch-based Descriptors for Image Copy Detection

Baber¹,

Bakhtyar²,

Noor³

et al. 2016

ijacsa

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Abstract-Images have become main sources for the information, learning, and entertainment, but due to the advancement and progress in multimedia technologies, millions of images are shared on Internet daily which can be easily duplicated and redistributed. Distribution of these duplicated and transformed images cause a lot of problems and challenges such as piracy, redundancy, and content-based image indexing and retrieval. To address these problems, copy detection system based on local features are widely used. Initially, keypoints are detected and represented by some robust descriptors. The descriptors are computed over the affine patches around the keypoints, these patches should be repeatable under photometric and geometric transformations. However, there exist two main challenges with patch based descriptors, (1) the affine patch over the keypoint can produce similar descriptors under entirely different scene or the context which causes "ambiguity", and (2) the descriptors are not enough "distinctive" under image noise. Due to these limitations, the copy detection systems suffer in performance. We present a framework that makes descriptor more distinguishable and robust by influencing them with the texture and gradients in vicinity. The experimental evaluation on keypoints matching and image copy detection under severe transformations shows the effectiveness of the proposed framework.

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