Texture Image Retrieval Using A Classification And Contourlet-based Features

Abstract

In this paper, we propose a new framework for improving Content Based Image Retrieval (CBIR) for texture images. This is achieved by using a new image representation based on the RCT-Plus transform which is a novel variant of the Redundant Contourlet transform that extracts a richer directional information in the image. Moreover, the process of image search is improved through a learning-based approach where the images of the database are classified using an adapted similarity metric to the statistical modeling of the RCT-Plus transform. A query is then first classified to select the best texture class after which the retained class images are ranked to select top ones. By this, we have achieved significant improvements in the retrieval rates compared to previous CBIR schemes.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyrouhafzay2024texture

Related papers

• Texture And Color-based Image Retrieval Using The Local Extrema Features And Riemannian Distance (2016)0.00
• Texture Retrieval Using Periodically Extended And Adaptive Curvelets (2019)7.16
• Automatic Feature Weight Determination Using Indexing And Pseudo-relevance Feedback For Multi-feature Content-based Image Retrieval (2018)0.00
• Image Retrieval Approach Based On Local Texture Information Derived From Predefined Patterns And Spatial Domain Information (2019)0.00
• Local Neighborhood Intensity Pattern: A New Texture Feature Descriptor For Image Retrieval (2017)14.11
• Content-based Image Retrieval System With Most Relevant Features Among Wavelet And Color Features (2019)0.00
• An Efficient Image Retrieval Based On Fusion Of Low-level Visual Features (2018)3.58
• Advancements In Content-based Image Retrieval: A Comprehensive Survey Of Relevance Feedback Techniques (2023)0.00