An efficient approach to texture‐based image retrieval

Abstract

In this article, we present an efficient approach for image retrieval based on the textural information of an image, such as orientation, directionality, and regularity. For this purpose, we apply the nonlinear modified discrete Radon transform to estimate these visual contents. We then utilize texture orientation to construct the rotated Gabor transform for extraction of the rotation-invariant texture feature. The rotation-invariant texture feature, directionality, and regularity are the main features used in the proposed approach for similarity assessment. Experimental results on a large number of texture and aerial images from standard databases show that the proposed schemes for feature extraction and image retrieval significantly outperform previous works, including methods based on the MPEG-7 texture descriptors.

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

“…Query image and images found in the repository are qualified as a collection of feature vectors and ranking of the relevant results occur on the basis of common norms, i.e., distance or semantic association by a machine learning technique [15]. Signature development is usually performed through the analysis of color [4,16], texture [17], or shape [19] or by generating any of these combinations and representing them mathematically [8]. Color features are extensively used in CBIR, which may be endorsed to the better potentiality in three dimensional domains over the gray level images which is single dimensional domain.…”

“…It depends on the primary colors in the small structures with similar edge orientation, which are simulated according to the human visual processing estimation. Hejazi and Ho [17] presented an image retrieval approach, which is based on textural information of an image and their approach considers the directionality, orientation and regularity of the texture using nonlinear modified discrete Radon transform, which are further used for image retrieval purposes. The Vector Quantization (VQ) is applied in the work of [18] for feature extraction.…”

“…Global image features are generated by considering the whole image as a single entity and then feature representations are obtained. Color histograms [6,16], texture [10,17,18], color layout [4,5] etc. are all examples of the global image features.…”

A Relevant Visual Feature Selection Approach for Image Retrieval

“…But it is worth noting that shape based approaches have higher complexity. In (Hejazi and Ho, 2007), an image retrieval approach based on classical texture features, such as orientation, directionality, and regularity has been proposed. Their discriminant power has been compared to the MPEG-7 texture feature through experimentation on aerial images.…”

Colour image retrieval using pattern co-occurrence matrices based on BTC and VQ

“…Early text-based image retrieval schemes utilised keywords for similar image retrieval, however they are impractical for modern image databases since the size of the databases is huge and different people may annotate the same image with different keywords. Unlike the traditional text-based image retrieval methods, content-based image retrieval (CBIR) methods index images based on their visual contents described by automatically extracted features, such as colour [1], texture [2], shape [3], and so on. Vector quantisation (VQ) and block truncation coding (BTC) are two typical block-based image coding schemes that can be used not only to efficiently compress images but also to extract compressed-domain features, and thus several VQbased [4][5][6] and BTC-based [7,8] image retrieval methods have been proposed recently.…”