Local quantized extrema patterns for content-based natural and texture image retrieval

Abstract

BackgroundData mining is an active area of research for mining or retrieving data/information from a large database or library. Image retrieval is the part of data mining in which the visual information (images) from the large size database or library is retrieved. Earlier, text-based retrieval was used for retrieving the information. In this process, the images are annotated with text and then text-based database management systems were used to perform image retrieval. Many advances, such as data modelling, multidimensional indexing, and query evaluation, have been made along this research direction. However, there exist two major difficulties, especially when the size of image collections is too large (tens or hundreds of thousands). One is the vast amount of labor required to annotate the images manually. The other difficulty, which is more essential, araise due to the rich content in the images and the subjectivity of human perception. That is, for Abstract This paper proposes a novel feature descriptor, named local quantized extrema patterns (LQEP) for content based image retrieval. The standard local quantized patterns (LQP) collect the directional relationship between the center pixel and its surrounding neighbors and the directional local extrema patterns (DLEP) collect the directional information based on local extrema in 0°, 45°, 90°, and 135° directions for a given center pixel in an image. In this paper, the concepts of LQP and DLEP are integrated to propose the LQEP for image retrieval application. First, the directional quantized information is collected from the given image. Then, the directional extrema is collected from the quantized information. Finally, the RGB color histogram is integrated with the LQEP for a feature vector generation. The performance of the proposed method is tested by conducting three experiments on Coel-1K, Corel-5K and MIT VisTex databases for natural and texture image retrieval. The performance of the proposed method is evaluated in terms of precision, recall, average retrieval precision and average retrieval rate on benchmark databases. The results after investigation show a considerable improvements in terms of their evaluation measures as compared to the existing methods on respective databases.

Biomedical Image Indexing and Retrieval Descriptors: A Comparative Study

“…The positions are indexed in a manner to write the four directional extremas operator calculations. Local directional extrema values 28 (8) Similarly, from above Eq. (7), lower LTP, DTEC2 is computed by obtaining value of as follows:…”

“…LBP, LDP and LTP capture the feature information based on the distribution of edges which are coded using only two directions (positive direction or negative direction). In some more researches, the local quantized patterns (LQP) for visual recognition 27 and local quantized extrema patterns (LQEP) for natural and texture image retrieval have proposed 28 . The local mesh patterns (LMeP) have been proposed for biomedical image retrieval 23 .…”

Local mesh quantized extrema patterns for image retrieval

“…The operators DLEP (Jhanwar et al 2004) and LQP (Vipparthi et al 2015) are integrated to propose the LQEP (Koteswara Rao and Venkata Rao 2015) for image retrieval. In the first step, the possible structures are extracted from the considered pattern of an image.…”

“…To address few issues of LBP, Hussain and trigges (2012) introduced local quantized patterns (LQP) for visual recognition. Verma et al (2015) proposed an integrated approach based on local patterns to natural, biomedical images (Koteswara Rao and Venkata Rao 2015). Vipparthi et al integrated Local patterns and Gabor feature to propose a feature descriptor (Verma et al 2015).…”

An extension of local mesh peak valley edge based feature descriptor for image retrieval in bio-medical images

“…These codes are generated based on the relationship between neighbors. Rao et al (Rao 2015) combined the DLEP and LQP to obtain the local quantized extrema pattern (LQEP) for classification. Deep et al in (Deep 2016) annexed an anti-diagonal descriptor to it for creating a better feature vector.…”