Scene search based on the adapted triangular regions and soft clustering to improve the effectiveness of the visual-bag-of-words model

Abstract

The storage size of the image and video repositories are growing day by day due to the extensive use of digital image acquisition devices. The position of an object within an image is obtained by analyzing the content-based properties like shape, texture, and color, while compositional properties present the image layout and include the photographic rule of composition. The high-quality images are captured on the basis of the rule of thirds that divide each image into nine square areas. According to this rule, salient objects of an image are placed on the intersection points or along the imagery lines of the grid to capture the position of the salient objects. To improve image retrieval performance, visual-bag-of-words (VBoW) framework-based image representation is widely used nowadays. According to this framework, the spatial relationship between salient objects of an image is lost due to the formation of a global histogram of the image. This article presents a novel adapted triangular area-based technique, which computes local intensity order pattern (LIOP) features, weighted soft codebooks, and triangular histograms from the four triangular areas of each image. The proposed technique adds the spatial contents from four adapted triangular areas of each image to the inverted index of the VBoW framework, solve overfitting problem of the larger sizes of the codebook, and overwhelmed the problem of the semantic gap. The experimental results and statistical analysis performed on five image collections show an encouraging robustness of the proposed technique that is compared with the recent CBIR techniques.

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

“…The intensity‐based feature is extracted from the RBCs and through these features artificial neural network classifier has been trained. Accordingly, to facilitate the use of the system the graphical user interface has been developed (Bashir, Mustafa, Abdelhameid, & Ibrahem, ; Mehmood et al, ; Sharif et al, ; Yousuf et al, ).The researcher proposed a system for automatic detection of malaria parasite from the desired images. This system employs image segmentation techniques to detect malaria parasites from images acquired from Giemsa stained peripheral blood samples (Ghate, Jadhav, & Rani, ).…”

“…Region growing algorithm starts by selecting an arbitrary seed pixel and match it with neighboring pixels. The region is grown from the seed pixel by adding in neighboring pixels that are alike, increasing the size of the region (Chaudhry, Rahim, Saba, & Rehman, ; Mehmood et al, ; Muhsin, Rehman, Altameem, Saba, & Uddin, ; Nodehi et al, ; Saba, Rehman, & Sulong, ). When the growth of one region halts, we simply select another seed pixel, which does not yet belong to any region and start again.…”

Spatio-Temporal Residual Networks for Slide Transition Detection in Lecture Videos

“…They identified slide title and detects texts by OCR technology to generate keyframes. Besides, some approaches are proposed to reduce the semantic gap across images [22][23][24]. Mehmood et al [22] presented an approach to reduce the semantic gap beteween low-level image features and high-level semantic concepts by collecting dense LIOP features and spatial histograms over four adapted triangular areas of an image.…”

“…Besides, some approaches are proposed to reduce the semantic gap across images [22][23][24]. Mehmood et al [22] presented an approach to reduce the semantic gap beteween low-level image features and high-level semantic concepts by collecting dense LIOP features and spatial histograms over four adapted triangular areas of an image. Sarwar et al [23] introduced a novel BoW model, which perform visual words integration of LIOP and LBPV features to reduce semantic gap across images.…”

A Local Feature Descriptor Based on SIFT for 3D Pollen Image Recognition

“…Achievements in feature extraction for automatic pollen recognition can be reviewed with two aspects: planar features in 2D images and stereoscopic features in 3D images. Prior researches generally confirm that planar features can effectively describe the shape and structures of two-dimensional pollen images and usually have good rotation invariance [21]- [24]. Rodriguez-Damian et al achieved 86% recognition rate in the dataset of the algorithm based on the combination of shape and texture analysis, which has been widely utilized in Urticaceae family.…”