Bundle min-hashing for logo recognition

Romberg, Stefan; Lienhart, Rainer

doi:10.1145/2461466.2461486

Cited by 73 publications

(59 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For keypoint-based methods, the spatial context among the local features is important to discriminative target objects from others, especially in cases of rigid objects. Examples include [118], [119], [120]. Other effective methods include burstiness handling [77] (discussed in Section 3.4.3), considering the different inlier ratios between the query and target objects [121], etc.…”

Section: Small Object Retrievalmentioning

confidence: 99%

SIFT Meets CNN: A Decade Survey of Instance Retrieval

Zheng

Yang

Tian

2018

IEEE Trans. Pattern Anal. Mach. Intell.

655

319

View full text Add to dashboard Cite

Abstract-In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.

show abstract

Section: Small Object Retrievalmentioning

confidence: 99%

SIFT Meets CNN: A Decade Survey of Instance Retrieval

Zheng

Yang

Tian

2018

IEEE Trans. Pattern Anal. Mach. Intell.

655

319

View full text Add to dashboard Cite

show abstract

“…As another application of LSH for logo detection, Romberg et al proposed the bundle min-hashing [9], [10]. This method calculates features which are more robust to appearance variation than single visual word by bundling visual word and spacial neighborhood features.…”

Section: Related Workmentioning

confidence: 99%

Extraction and Recognition of Shoe Logos with a Wide Variety of Appearance Using Two-Stage Classifiers

Aoki

Ohyama

Wakabayashi

2018

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

Kazunori AOKI†a) , Nonmember, Wataru OHYAMA †b) , and Tetsushi WAKABAYASHI †c) , Members SUMMARY A logo is a symbolic presentation that is designed not only to identify a product manufacturer but also to attract the attention of shoppers. Shoe logos are a challenging subject for automatic extraction and recognition using image analysis techniques because they have characteristics that distinguish them from those of other products; that is, there is much within-class variation in the appearance of shoe logos. In this paper, we propose an automatic extraction and recognition method for shoe logos with a wide variety of appearance using a limited number of training samples. The proposed method employs maximally stable extremal regions for the initial region extraction, an iterative algorithm for region grouping, and gradient features and a support vector machine for logo recognition. The results of performance evaluation experiments using a logo dataset that consists of a wide variety of appearances show that the proposed method achieves promising performance for both logo extraction and recognition. key words: logo extraction, logo recognition, maximally stable extremal regions, gradient histogram features, support vector machine

show abstract

“…For further improvement on the retrieval performance, both approaches added the spatial verification stage to re-rank the results in order to remove noisy or ambiguous visual words. Recent works in [7], [10], [9] and [8] extended the BoVW approach by encoding the geometric information around the local features into the representation and refine the matching based on visual words. Those methods were very sensitive to the change in imaging condition and made them only suitable for partial-duplicate image search.…”

Section: Related Workmentioning

confidence: 99%

Instance Search with Weak Geometric Correlation Consistency

Zhang

Albatal

Gurrin

et al. 2016

MultiMedia Modeling

View full text Add to dashboard Cite

Abstract. Finding object instances from large image collections is a challenging problem with many practical applications. Recent methods inspired by text retrieval achieved good results; however a re-ranking stage based on spatial verification is still required to boost performance. To improve the effectiveness of such instance retrieval systems while avoiding the computational complexity of a re-ranking stage, we explored the geometric correlations among local features and incorporate these correlations with each individual match to form a transformation consistency in rotation and scale space. This weak geometric correlation consistency can be used to effectively eliminate inconsistent feature matches and can be applied to all candidate images at a low computational cost. Experimental results on three standard evaluation benchmarks show that the proposed approach results in a substantial performance improvement compared with recent proposed methods.

show abstract

Bundle min-hashing for logo recognition

Cited by 73 publications

References 19 publications

SIFT Meets CNN: A Decade Survey of Instance Retrieval

SIFT Meets CNN: A Decade Survey of Instance Retrieval

Extraction and Recognition of Shoe Logos with a Wide Variety of Appearance Using Two-Stage Classifiers

Instance Search with Weak Geometric Correlation Consistency

Contact Info

Product

Resources

About