A survey on image retrieval performance of different bag of visual words indexing techniques

Mukherjee, Jit; Mukhopadhyay, Jayanta; Mitra, Pabitra

doi:10.1109/techsym.2014.6807922

Cited by 15 publications

(3 citation statements)

References 23 publications

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“…A total of 500 visual words was obtained, each of which is determined by the center of the clustered feature descriptors in the known category of body shapes (inverted triangle, pear, hourglass, and rectangle) using k-means clustering [32]. In our body detection experiment, the numbers of descriptors and clusters were around 78,000 and 500 words, respectively, and the dimension of each word was 64 [42]. The visual words were then included in a visual dictionary.…”

Section: Number Of Features = Number Of Category × mentioning

confidence: 99%

SmartFit: Smartphone Application for Garment Fit Detection

et al. 2021

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The apparel e-commerce industry is growing day by day. In recent times, consumers are particularly interested in an easy and time-saving way of online apparel shopping. In addition, the COVID-19 pandemic has generated more need for an effective and convenient online shopping solution for consumers. However, online shopping, particularly online apparel shopping, has several challenges for consumers. These issues include sizing, fit, return, and cost concerns. Especially, the fit issue is one of the cardinal factors causing hesitance and drawback in online apparel purchases. The conventional method of clothing fit detection based on body shapes relies upon manual body measurements. Since no convenient and easy-to-use method has been proposed for body shape detection, we propose an interactive smartphone application, “SmartFit”, that will provide the optimal fitting clothing recommendation to the consumer by detecting their body shape. This optimal recommendation is provided by using image processing and machine learning that are solely dependent on smartphone images. Our preliminary assessment of the developed model shows an accuracy of 87.50% for body shape detection, producing a promising solution to the fit detection problem persisting in the digital apparel market.

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Section: Number Of Features = Number Of Category × mentioning

confidence: 99%

SmartFit: Smartphone Application for Garment Fit Detection

et al. 2021

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“…In [78], authors have presented Sphere/Rectangle Tree indexing and locality sensitive hashing techniques with bag of visual words. SURF is used to describe the image features.…”

Section: Feature Fusion-based Techniques Used Inmentioning

confidence: 99%

Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

Dewan¹,

Thepade²

2020

Applied Computational Intelligence and Soft Computing

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Billions of multimedia data files are getting created and shared on the web, mainly social media websites. The explosive increase in multimedia data, especially images and videos, has created an issue of searching and retrieving the relevant data from the archive collection. In the last few decades, the complexity of the image data has increased exponentially. Text-based image retrieval techniques do not meet the needs of the users due to the difference between image contents and text annotations associated with an image. Various methods have been proposed in recent years to tackle the problem of the semantic gap and retrieve images similar to the query specified by the user. Image retrieval based on image contents has attracted many researchers as it uses the visual content of the image such as color, texture, and shape feature. The low-level image features represent the image contents as feature vectors. The query image feature vector is compared with the dataset images feature vectors to retrieve similar images. The main aim of this article is to appraise the various image retrieval methods based on feature extraction, description, and matching content that has been presented in the last 10–15 years based on low-level feature contents and local features and proposes a promising future research direction for researchers.

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“…Therefore, the construction of image signatures is a key step and the core of a CBIR system. The state of the art mentions two main approaches used to retrieve the closest images: BoVW [57] (Bag of Visual Words) and CNN [58] (Convolutional Neural Networks) descriptors for image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Leveraging semantic segmentation for hybrid image retrieval methods

Ouni

Royer

Chevaldonné

et al. 2021

Neural Comput & Applic

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Content Based Image Retrieval (CBIR) is the task of finding images in a database that are the most similar to the input query based on its visual characteristics. Several methods from the state of the art based on visual methods (Bag of visual words, VLAD, ...) or recent deep leaning methods try to solve the CBIR problem. In particular, Deep learning is a new field and used for several vision applications including CBIR. But, even with the increase of the performance of deep learning algorithms, this problem is still a challenge in computer vision. In this work, we propose three different methodologies combining deep learning based semantic segmentation and visual features. We show experimentally that by exploiting semantic information in the CBIR context leads to an increase in the retrieval accuracy. We study the performance of the proposed approach on eight different datasets (Wang, Corel-10k, Corel-5k, GHIM-10K, MSRC V1, MSRC V2, Linnaeus, NUS-WIDE)

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A survey on image retrieval performance of different bag of visual words indexing techniques

Cited by 15 publications

References 23 publications

SmartFit: Smartphone Application for Garment Fit Detection

SmartFit: Smartphone Application for Garment Fit Detection

Image Retrieval Using Low Level and Local Features Contents: A Comprehensive Review

Leveraging semantic segmentation for hybrid image retrieval methods

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