Local Tetra-Directional Pattern–A New Texture Descriptor for Content-Based Image Retrieval

PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

“…Recently, a local tetra-directional pattern was introduced by Bedi et al [63], in which they compared a pixel with the four most adjacent pixels (0 • , 45 • , 90 • , and 135 • ). Among all the above-described descriptors, the most appropriate was the local tridirectional or tetra-directional patterns.…”

“…Each of the eight pixels are as important as the three angles used by [51] or the four angles used by [63]. For example, in Figure 2, considering the tri directional pattern for location I1, value 8 is only compared with the above, below, and central pixels, whereas we have higher intensity pixels available in the second radius, which could be used in making the strongest feature vectors which were left behind; on the other hand, in the tetra directional pattern, the second radius is not fully utilized; in I1, value 8 is only compared with angles (0 • , 45 • , 90 • , and 135 • ) which are (2, 8, 9 and 4), and again we have high intensity pixels available at angles 270 • and 315 • , which were left behind.…”

“…For example, in Figure 2, considering the tri directional pattern for location I1, value 8 is only compared with the above, below, and central pixels, whereas we have higher intensity pixels available in the second radius, which could be used in making the strongest feature vectors which were left behind; on the other hand, in the tetra directional pattern, the second radius is not fully utilized; in I1, value 8 is only compared with angles (0 • , 45 • , 90 • , and 135 • ) which are (2, 8, 9 and 4), and again we have high intensity pixels available at angles 270 • and 315 • , which were left behind. The second drawback is that Bedi [63] utilized the second radius, but he did not mention any methodology pertaining to how to handle image edges where use of the second radius is not possible. The third disadvantage of both approaches is the need to stick with the Euclidian distance calculation approach while the accuracy of Manhattan distance is proven [64].…”

“…The advanced local octa-directional pattern (ALODP-PCA) not only utilizes all the surrounding pixel information, but also reduces the computation cost as well. Instead of taking 3 directions [52] or 4 directions [63], all of the directions in the first degree radius are utilized in ALODP-PCA. True information about any pixel is retained by its closest neighboring pixels.…”

Guided Deep Learning by Subaperture Decomposition: Ocean Patterns from SAR Imagery

“…Ideally, the similarity score between two images should be discriminative, robust and efficient. Various methods based on hand-crafted descriptors [32], [33], [34], distance metric learning [35], [36], [37], deep learning models [38], [9] and unsupervised learning [11], [12], [13], [14] have been proposed for the image retrieval task. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering, the features being learned automatically from data.…”