A Contour-Based Method for Logo Detection

Pham, The-Anh; Delalandre, Matthieu; Barrat, Sabine

doi:10.1109/icdar.2011.150

Cited by 18 publications

(8 citation statements)

References 11 publications

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“…In recent years, there has been a number of approaches proposed for logo detection [11,16,17,18], logo recognition [19,20] and logo spotting [6,13,7,8]. In the field of logo retrieval, M. Rusinol et al [14] introduce a method for organizing and indexing logos based on a description using a variant of the shape context descriptor.…”

Section: Introductionmentioning

confidence: 99%

“…Different techniques in graphics recognition have been employed and developed throughout research works. Learning-based methods in [11,13,18], methods based on shape context descriptor [14,12], methods based on context-dependent keypoint matching [10], and key-point matching method [3,6] were popular. Among these, a key-point matching method using the nearest neighbor matching rule with ambiguity rejection based on the two nearest neighbors, has achieved good results in [3,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Document Retrieval Based on Logo Spotting Using Key-Point Matching

Le¹,

Nayef²,

Visani³

et al. 2014

2014 22nd International Conference on Pattern Recognition

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In this paper, we present an approach to retrieve documents based on logo spotting and recognition. A document retrieval system is proposed inspired from our previous method for logo spotting and recognition. First, the key-points from both the query logo images and a given set of document images are extracted and described by SIFT descriptor, and are matched in the SIFT feature space. They are filtered by the nearest neighbor matching rule based on the two nearest neighbors and are then post-filtered with BRIEF descriptor. Secondly, logo segmentation is performed using spatial density-based clustering, and homography is used to filter the matched key-points as a postprocessing. Finally, for ranking, we use two measures which are calculated based on the number of matched key-points. Tested on a well-known benchmark database of real world documents containing logos Tobacco-800, our approach achieves better performance than the state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Document Retrieval Based on Logo Spotting Using Key-Point Matching

Le¹,

Nayef²,

Visani³

et al. 2014

2014 22nd International Conference on Pattern Recognition

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show abstract

“…Furthermore, GAB is more numerically stable and robust to noise, for it puts less emphasis on outliers. Additionally, GAB is known as one of the best out of box supervised regression algorithm [15].…”

Section: Estimating T-link Weights By Gentle Adaboostmentioning

confidence: 99%

A robust road segmentation method based on graph cut with learnable neighboring link weights

Yuan

Tang

Wang

et al. 2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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Road region detection is a crucial functionality for road following in advanced driver assistance systems (ADAS). To address the problem of environment interference in road segmentation through a monocular vision approach, a novel graph-cut based method is proposed in this paper. The novelty of this proposal is that weights of neighboring links (n-links) in a s-t graph are estimated by Multilayer Perceptrons (MLPs) rather than calculating by the neighboring contrast simply in previous graph-cut based methods. Estimating n-link weights by MLPs reinforces the ability of graph-cut based road segmentation algorithms to tolerate the complex and changeable appearance of road surfaces. Additionally, the Gentle AdaBoost algorithm is integrated into the graph-cut framework to estimate the terminal link (t-link) weights in the s-t graph. Experiments are conducted to show the robustness and efficiency of the proposed method.

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“…Many different techniques in pattern recognition and spotting have been employed and developed throughout research work. Learning-based methods in [12,14,19], methods based on shape context descriptor [15,13], methods based on context-dependent key-point matching [11], and keypoint matching method [3,6] were popular. Among these, a key-point matching method using the nearest neighbor matching rule with ambiguity rejection based on the two nearest neighbors, has achieved good results in [3,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…A number of approaches for logo detection [12,17,18,19], logo recognition [20,21] and logo spotting [6,14,7,8] are proposed. Many different techniques in pattern recognition and spotting have been employed and developed throughout research work.…”

Section: Introductionmentioning

confidence: 99%

Key-point matching with post-filter using SIFT and BRIEF in logo spotting

Tran

2015

The 2015 IEEE RIVF International Conference on Computing &Amp; Communication Technologies - Research, Innovation, and Vision Fo

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In this paper, a method to spot and recognize logos based on key-point matching is proposed. It is applied and tested on a document retrieval system. First, the pairs of matched keypoints are estimated by the nearest neighbor matching rule based on the two nearest neighbors in SIFT descriptor space with Euclidean distance. Second, a post-filter with BRIEF descriptor space and hamming distance is used to re-filter the key-points which are rejected by the first step. Tested on a well-known benchmark database of real world documents containing logos Tobacco-800, our method performs an increase in the number of matched key-points of the method combined with BRIEF postfilter at the same accuracy level, and achieves a better performance than the state-of-the-art methods in the field of document retrieval.

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A Contour-Based Method for Logo Detection

Cited by 18 publications

References 11 publications

Document Retrieval Based on Logo Spotting Using Key-Point Matching

Document Retrieval Based on Logo Spotting Using Key-Point Matching

A robust road segmentation method based on graph cut with learnable neighboring link weights

Key-point matching with post-filter using SIFT and BRIEF in logo spotting

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