A Method of Protein Model Classification and Retrieval Using Bag-of-Visual-Features

In this paper we propose a novel visual method for protein model classification and retrieval. Different from the conventional methods, the key idea of the proposed method is to extract image features of proteins and measure the visual similarity between proteins. Firstly, the multiview images are captured by vertices and planes of a given octahedron surrounding the protein. Secondly, the local features are extracted from each image of the different views by the SURF algorithm and are vector quantized into visual words using a visual codebook. Finally, KLD is employed to calculate the similarity distance between two feature vectors. Experimental results show that the proposed method has encouraging performances for protein retrieval and categorization as shown in the comparison with other methods

COMPARING ROTATION-ROBUST MECHANISMS IN LOCAL FEATURE MATCHING: HAND-CRAFTED VS. DEEP LEARNING ALGORITHMS

The objective of this research is to conduct a performance comparison between hand-crafted feature matching algorithms and deep learning-based counterparts in the context of rotational variances. Hand-crafted algorithms underwent testing utilizing FLANN (Fast Library for Approximate Nearest Neighbors) as the matcher and RANSAC (Random sample consensus) for outlier detection and elimination, contributing to enhanced accuracy in the results. Surprisingly, experiments revealed that hand-crafted algorithms could yield comparable or superior results to deep learning-based algorithms when exposed to rotational variances. Notably, the application of horizontally flipped images showcased a distinct advantage for deep learning-based algorithms, demonstrating significantly improved results compared to their hand-crafted counterparts. While deep learning-based algorithms exhibit technological advancements, the study found that hand-crafted algorithms like AKAZE and AKAZE-SIFT could effectively compete with their deep learning counterparts, particularly in scenarios involving rotational variances. However, the same level of competitiveness was not observed in horizontally flipped cases, where hand-crafted algorithms exhibited suboptimal results. Conversely, deep learning algorithms such as DELF demonstrated superior results and accuracy in horizontally flipped scenarios. The research underscores that the choice between hand-crafted and deep learning-based algorithms depends on the specific use case. Hand-crafted algorithms exhibit competitiveness, especially in addressing rotational variances, while deep learning-based algorithms, exemplified by DELF, excel in scenarios involving horizontally flipped images, showcasing the unique advantages each approach holds in different contexts

An algorithm for the contextual adaption of SURF octave selection with good matching performance: best octaves.

Speeded-Up Robust Features is a feature extraction algorithm designed for real-time execution, although this is rarely achievable on low-power hardware such as that in mobile robots. One way to reduce the computation is to discard some of the scale-space octaves, and previous research has simply discarded the higher octaves. This paper shows that this approach is not always the most sensible and presents an algorithm for choosing which octaves to discard based on the properties of the imagery. Results obtained with this best octaves algorithm show that it is able to achieve a significant reduction in computation without compromising matching performance

Rotation Invariant on Harris Interest Points for Exposing Image Region Duplication Forgery

Nowadays, image forgery has become common because only an editing package software and a digital camera are required to counterfeit an image. Various fraud detection systems have been developed in accordance with the requirements of numerous applications and to address different types of image forgery. However, image fraud detection is a complicated process given that is necessary to identify the image processing tools used to counterfeit an image. Here, we describe recent developments in image fraud detection. Conventional techniques for detecting duplication forgeries have difficulty in detecting postprocessing falsification, such as grading and joint photographic expert group compression. This study proposes an algorithm that detects image falsification on the basis of Hessian features

Numerical Simulation and Design of Copy Move Image Forgery Detection Using ORB and K Means Algorithm

Copy-move is a common technique for tampering with images in the digital realm. Therefore, image security authentication is of critical importance in our society. So copy move forgery detection (CMFD) is activated in order to identify the forged portion of a photograph. A combination of the Scaled ORB and the k-means++ algorithm is used to identify this object. The first step is to identify the space on a pyramid scale, which is critical for the next step. A region's defining feature is critical to its detection. Because of this, the ORB descriptor plays an important role. Extracting FAST key points and ORB features from each scale space. The coordinates of the FAST key points have been reversed in relation to the original image. The ORB descriptors are now subjected to the k-means++ algorithm. Hammering distance is used to match the clustered features every two key points. Then, the forged key points are discovered. This information is used to draw two circles on the forged and original regions. Moment must be calculated if the forged region is rotational invariant. Geometric transformation (scaling and rotation) is possible in this method. For images that have been rotated and smoothed, this work demonstrates a method for detecting the forged region. The running time of the proposed method is less than that of the previous method