Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

Bhangale*, Kishor B.; K., Mohanaprasad

doi:10.35940/ijitee.b8014.019320

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…By segmenting the images and calculating the distribution of local intensity gradients, the local HOG descriptor extracts the shape of objects present in the images. Being a robust technique in relation to brightness variations and the presence of shadows, it has been explored by some researchers, alone or combined with other descriptors, [ 88 – 90 ], demonstrating good results, particularly in object recognition. However, the use of HOG prevents multispectral images from being directly used.…”

Section: Related Workmentioning

confidence: 99%

DarwinGSE: Towards better image retrieval systems for intellectual property datasets

António,

Valente,

Mora

et al. 2024

PLoS ONE

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A trademark’s image is usually the first type of indirect contact between a consumer and a product or a service. Companies rely on graphical trademarks as a symbol of quality and instant recognition, seeking to protect them from copyright infringements. A popular defense mechanism is graphical searching, where an image is compared to a large database to find potential conflicts with similar trademarks. Despite not being a new subject, image retrieval state-of-the-art lacks reliable solutions in the Industrial Property (IP) sector, where datasets are practically unrestricted in content, with abstract images for which modeling human perception is a challenging task. Existing Content-based Image Retrieval (CBIR) systems still present several problems, particularly in terms of efficiency and reliability. In this paper, we propose a new CBIR system that overcomes these major limitations. It follows a modular methodology, composed of a set of individual components tasked with the retrieval, maintenance and gradual optimization of trademark image searching, working on large-scale, unlabeled datasets. Its generalization capacity is achieved using multiple feature descriptions, weighted separately, and combined to represent a single similarity score. Images are evaluated for general features, edge maps, and regions of interest, using a method based on Watershedding K-Means segments. We propose an image recovery process that relies on a new similarity measure between all feature descriptions. New trademark images are added every day to ensure up-to-date results. The proposed system showcases a timely retrieval speed, with 95% of searches having a 10 second presentation speed and a mean average precision of 93.7%, supporting its applicability to real-word IP protection scenarios.

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Section: Related Workmentioning

confidence: 99%

DarwinGSE: Towards better image retrieval systems for intellectual property datasets

António,

Valente,

Mora

et al. 2024

PLoS ONE

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Recent Trends and Techniques of CBIR to Enhance Retrieval Performance

Ugale

Mali

2022

Lecture Notes in Electrical Engineering

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3D Indoor Scene Synthesis System Based on Collaborative Retrieval

Weijun

2022

Wireless Communications and Mobile Computing

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In order to solve the research problem of collaborative retrieval 3D indoor scene synthesis system, an automatic synthesis method of 3D indoor scene is proposed. In the research fields of computer graphics and computer vision, the modeling of various 3D scenes has always been a hot spot and a very valuable research topic for Chinese and foreign scholars. GMM is used to fit the position of the object on the support surface, and the position distribution model is obtained. Firstly, a file is created to store the normalized position of the counted object on the support surface. The number of coordinates represents the number of times the object appears in the scene library. Then, the Gaussian mixture model is used to fit the content of the file to obtain the location distribution model. For the initial indoor scene and the description of small objects to be added to the scene given by the user, select small objects with the help of the material library. Shapenetcorcv2 is the core part of the database, with a total of 55 categories and more than 50000 model instances. The automatic placement of these small objects is realized by using the scene synthesis algorithm, which shows the continuous enrichment of the evolution process of simple scenes to solve various problems of 3D indoor scene modeling. This method not only reflects the design requirements of users but also avoids cumbersome manual operation.

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Content Based Image Retrieval using Collaborative Color, Texture and Shape Features

Cited by 4 publications

References 10 publications

DarwinGSE: Towards better image retrieval systems for intellectual property datasets

DarwinGSE: Towards better image retrieval systems for intellectual property datasets

Recent Trends and Techniques of CBIR to Enhance Retrieval Performance

3D Indoor Scene Synthesis System Based on Collaborative Retrieval

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