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Journal ArticleDOI

A novel scale and rotation invariant texture image retrieval method using fuzzy logic classifier

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TLDR
The scale and rotation invariant texture retrieval is an incomparable work that has been demonstrated in the present paper, and outperforms the existing methods in terms of average retrieval rate.
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This article is published in Computers & Electrical Engineering.The article was published on 2014-11-01. It has received 9 citations till now. The article focuses on the topics: Image texture & Image retrieval.

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Citations
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Journal ArticleDOI

Rotation and scale invariant hybrid image descriptor and retrieval

TL;DR: An efficient approach is presented to encode the color and textural features of images from the local neighborhood of each pixel to construct the inherently rotation and scale-invariant hybrid image descriptor (RSHD).
Proceedings ArticleDOI

LifeSeeker 3.0: An Interactive Lifelog Search Engine for LSC'21

TL;DR: The LifeSeeker 3.0 interactive lifelog retrieval engine is an enhanced version of the previous system participating in LSC'20 -LifeSeeker 2.0 as mentioned in this paper, which focuses on searching and filtering by text query using a weighted Bag-of-Words model with visual concept augmentation and three weighted vocabularies.
Proceedings ArticleDOI

A novel approach for content based image retrieval in context of combination S C techniques

TL;DR: This paper presents content based image retrieval (CBIR) system using immense power of combination of soft computing techniques such as artificial neural network, fuzzy logic and support vector machine that enormously improves accuracy of image retrieval.
Proceedings ArticleDOI

Local Directional Gradient Based Feature learning for Image Retrieval

TL;DR: Proposed feature descriptor outperforms existing state-of-the-art local feature descriptors in image retrieval accuracy measurement on publicly available (natural and medical) image databases.
Journal ArticleDOI

An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

TL;DR: To enhance the retrieval accuracy of the proposed system, a framework of an Extreme learning machine (ELM) with Relevance feedback (RF) has also been used to provide simultaneously spatial relationship and information related to frequency in co-occuring local patterns of an image.
References
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Book

Fuzzy sets

TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Journal ArticleDOI

Textural Features for Image Classification

TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI

Texture features for browsing and retrieval of image data

TL;DR: Comparisons with other multiresolution texture features using the Brodatz texture database indicate that the Gabor features provide the best pattern retrieval accuracy.
Journal ArticleDOI

Image retrieval: Ideas, influences, and trends of the new age

TL;DR: Almost 300 key theoretical and empirical contributions in the current decade related to image retrieval and automatic image annotation are surveyed, and the spawning of related subfields are discussed, to discuss the adaptation of existing image retrieval techniques to build systems that can be useful in the real world.
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