Adaptive pedestrian tracking via patch-based features and spatial-temporal similarity measurement

Content integrity of high-resolution remote sensing (HRRS) images is the premise of its usability. Existing HRRS image integrity authentication methods are mostly binary decision-making processes, which cannot provide a further interpretable information (e.g., tamper localization, tamper type determination). Due to this reason, a robust HRRS images integrity authentication algorithm using perceptual hashing technology considering both global and local features is proposed in this paper. It extracts global features by the efficient recognition ability of Zernike moments to texture information. Meanwhile, Features from Accelerated Segment Test (FAST) key points are applied to local features construction and tamper localization. By applying the concept of multi-feature combination to the integrity authentication of HRRS images, the authentication process is more convincing in comparison to existing algorithms. Furthermore, an interpretable authentication result can be given. The experimental results show that the algorithm proposed in this paper is highly robust to the content retention operation, has a strong sensitivity to the content changing operations, and the result of tampering localization is more precise comparing with existing algorithms.

High-Resolution Remote Sensing Image Integrity Authentication Method Considering Both Global and Local Features

With the massive growth of World Wide Web people are able to gain the access to massive amount of information. This information may contain data, images, flow charts, logos, maps etc. However locating and finding the relevant information is always a difficult task. A lot of textual-based search engines are available, but there are very rare image search engines through which desired images can be searched from the pool of images available on the web. Hence there is a need of such a image search engine using which the related and exact images can be searched. Reverse image search is such a content-based image retrieval query technique that involves providing the CBIR system with a sample image that it will then base its search upon. Reverse image search can be used to search either data related to the query image or the images related to that image or exact images.

Evaluation of compact composite descriptor based reverse image search

Image plagiarism can be detected using similarity between the two images. The perceptual hash function calculates similar hash value for similar images. This paper proposes a new modification to perceptual hashing (pHash) based approach for Image Retrieval. By the simplified use of a distance function two perceptual hash values can be compared and, it can be understood whether the images being compared are perceptually different or not. Existing Image Plagiarism detection techniques are not sensitive to image rotation. We propose an algorithm that compares the various degrees of rotation of the source image to the perceived similar image in an efficient way. Perceptual image hash functions along with the rotation check can be used for the identification of plagiarised images, authentication or integrity verification of images.

http://ijcstjournal.org/volume-2/issue-2/IJCST-V2I2P7.pdf

Mitigation of Rotational Constraints in Image Based Plagiarism Detection Using Perceptual Hash

Image search is becoming an urgent problem of the next generation of search engine. We firstly review the developed situation of image search engine in this paper. Then, the main difficulty and key technologies about this engine are analyzed. Next, the design method is elaborated in detail, which mainly includes image recognition, perceptual hash algorithm, system solution, image retrieval procedure as well as software module, and so on. As a result, we develop an image search engine according to above design methods and implement searching image on the Internet. The testing results finally prove the overall performance of our image search engine is excellent and achieves the desired design requirements. By using data filtering technology and perceptual hash algorithm, the search time-consumed is less than 1 second and is of high search efficiency.

/pdf/design-and-implementation-of-image-search-algorithm-2ubpoemil5.pdf

Design and Implementation of Image Search Algorithm

This paper proposes a new perceptual hashing based approach for Image Retrieval. Information in form of images is increasing at a very fast rate therefore there is need for faster and effective retrieval of specific image from image repositories. Content Based Image Retrieval (CBIR) is considered most efficient to search large image collections. The proposed method uses perceptual hash function which calculates similar hash value for similar images. Finally, using an adequate distance or similarity function to compare two perceptual hash values, it can be decided whether two images are perceptually different or not. Perceptual image hash functions can be used e.g. for the identification, authentication or integrity verification of images. In this paper we also propose a hash algorithm that is robust and secure to non malicious manipulation and sensitive to the malicious tampering. Keywords - Content based Image Retrieval (CBIR), Perceptual Hash algorithm, Image Retrieval using Perceptual Hashing, Query by Image Content (QBIC).

/pdf/image-retrieval-using-perceptual-hashing-4tvpu9ub78.pdf

Harsh Kumar Sarohi

Papers

Image Retrieval using Perceptual Hashing