The stanford mobile visual search data set

Chandrasekhar, Vijay; Chen, David M.; Tsai, Shiou‐Chuan; Cheung, Ngai-Man; Chen, Huizhong; Takacs, Gabriel; Reznik, Yuriy A.; Vedantham, Ramakrishna; Grzeszczuk, Radek; Bach, Jeff; Girod, Bernd

doi:10.1145/1943552.1943568

Cited by 129 publications

(73 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to evaluate the performance of the proposed matching method, the Stanford Mobile Visual Search (SMVS) dataset from Stanford University was used [21]. SMVS includes images of CDs, DVDs, books, paintings, and video clips, and is currently the standard image set for performance evaluation of image matching under MPEG-7 CDVS.…”

Section: Experiments Resultsmentioning

confidence: 99%

Adaptive Image Matching Using Discrimination of Deformable Objects

et al. 2016

View full text Add to dashboard Cite

Abstract:We propose an efficient image-matching method for deformable-object image matching using discrimination of deformable objects and geometric similarity clustering between feature-matching pairs. A deformable transformation maintains a particular form in the whole image, despite local and irregular deformations. Therefore, the matching information is statistically analyzed to calculate the possibility of deformable transformations, and the images can be identified using the proposed method. In addition, a method for matching deformable object images is proposed, which clusters matching pairs with similar types of geometric deformations. Discrimination of deformable images showed about 90% accuracy, and the proposed deformable image-matching method showed an average 89% success rate and 91% accuracy with various transformations. Therefore, the proposed method robustly matches images, even with various kinds of deformation that can occur in them.

show abstract

Section: Experiments Resultsmentioning

confidence: 99%

Adaptive Image Matching Using Discrimination of Deformable Objects

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Without pretending to be exhaustive in our overview, we mention that ALOI dataset containing 1000 objects [1], the ImageNet dataset containing 200 categories of objects [2], the dataset from the PASCAL 2012 challenge containing 20 classes of objects [3] and probably the closest to our application, the Stanford Mobile visual search data set contains 23 different objects such as books, CD covers, DVD covers and common objects [4].…”

Section: A State-of-the-art In the Package Databasesmentioning

confidence: 99%

PharmaPack: Mobile fine-grained recognition of pharma packages

Taran¹,

Rezaeifar²,

Dabrowski³

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Abstract-We consider the problem of fine-grained physical object recognition and introduce a dataset PharmaPack containing 1000 unique pharma packages enrolled in a controlled environment using consumer mobile phones as well as several recognition sets representing various scenarios. For performance evaluation, we extract two types of recently proposed local feature descriptors and aggregate them using popular tools. All enrolled raw and pre-processed images, extracted and aggregated descriptors are made public to promote reproducible research. To evaluate the baseline performance, we compare the methods based on aggregation of local descriptors with methods based on geometrical matching.

show abstract

“…Their approach computes Fisher vectors by aggregating ORB descriptors under the assumption that they are generated from a Bernoulli mixture model [12]. Fisher vectors composed of ORB descriptors were compared with bag of binary words [13] on the Stanford mobile visual search dataset (SMVSD) [14]. Although they were found to be better than the competitor, they did not compare their approach with the typical VLAD.…”

Section: Local Descriptor Extraction and Descriptor Aggregationmentioning

confidence: 99%

“…Next, we studied how much our framework accelerates the image retrieval process and reduces the amount of storage required compared with other approaches. We conducted experiments on three datasets, holidays [24], ukbench [25], SMVSD † [14]. All images were resized to less than 480 pixels beforehand.…”

Section: Overview and Protocolmentioning

confidence: 99%

Image Retrieval Framework Based on Dual Representation Descriptor

Yoshida

Toyofuku

2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYDescriptor aggregation techniques such as the Fisher vector and vector of locally aggregated descriptors (VLAD) are used in most image retrieval frameworks. It takes some time to extract local descriptors, and the geometric verification requires storage if a real-valued descriptor such as SIFT is used. Moreover, if we apply binary descriptors to such a framework, the performance of image retrieval is not better than if we use a real-valued descriptor. Our approach tackles these issues by using a dual representation descriptor that has advantages of being both a real-valued and a binary descriptor. The real value of the dual representation descriptor is aggregated into a VLAD in order to achieve high accuracy in the image retrieval, and the binary one is used to find correspondences in the geometric verification stage in order to reduce the amount of storage needed. We implemented a dual representation descriptor extracted in semi-real time by using the CARD descriptor. We evaluated the accuracy of our image retrieval framework including the geometric verification on three datasets (holidays, ukbench and Stanford mobile visual search). The results indicate that our framework is as accurate as the framework that uses SIFT. In addition, the experiments show that the image retrieval speed and storage requirements of our framework are as efficient as those of a framework that uses ORB. key words: image retrieval, local descriptor

show abstract

The stanford mobile visual search data set

Cited by 129 publications

References 17 publications

Adaptive Image Matching Using Discrimination of Deformable Objects

Adaptive Image Matching Using Discrimination of Deformable Objects

PharmaPack: Mobile fine-grained recognition of pharma packages

Image Retrieval Framework Based on Dual Representation Descriptor

Contact Info

Product

Resources

About