Image retrieval using noisy query

In conventional content based image retrieval (CBIR) employing relevance feedback, one implicit assumption is that both pure positive and negative examples are available. However it is not always true in the practical applications of CBIR. In this paper, we address a new problem of image retrieval using several unclean positive examples, named noisy query, in which some mislabeled images or weak relevant images present. The proposed image retrieval scheme measures the image similarity by combining multiple feature distances. Incorporating data cleaning and noise tolerant classifier, a twostep strategy is proposed to handle noisy positive examples. Experiments carried out on a subset of Corel image collection show that the proposed scheme outperforms the competing image retrieval schemes.<br /

Query generation from multiple media examples

This paper exploits an unified media document representation called feature terms for query generation from multiple media examples, e.g. images. A feature term refers to a value interval of a media feature. A media document is therefore represented by a frequency vector about feature term appearance. This approach (1) facilitates feature accumulation from multiple examples; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Three statistical criteria, minimised chi-squared, minimised AC/DC rate and maximised entropy, are proposed to extract feature terms from a given media document collection. Two textual ranking functions, KL divergence and a BM25-like retrieval model, are adapted to estimate media document relevance. Experiments on the Corel photo collection and the TRECVid 2006 collection show the effectiveness of feature term based query in image and video retrieval

Towards Privacy and Security Concerns of Adversarial Examples in Deep Hashing Image Retrieval

With the explosive growth of images on the internet, image retrieval based on deep hashing attracts spotlights from both research and industry communities. Empowered by deep neural networks (DNNs), deep hashing enables fast and accurate image retrieval on large-scale data. However, inheriting from deep learning, deep hashing remains vulnerable to specifically designed input, called adversarial examples. By adding imperceptible perturbations on inputs, adversarial examples fool DNNs to make wrong decisions. The existence of adversarial examples not only raises security concerns for real-world deep learning applications, but also provides us with a technique to confront malicious applications. In this dissertation, we investigate privacy and security concerns in deep hashing image retrieval systems related to adversarial examples. Starting with a privacy concern, we stand on users side to preserve privacy information in images, which can be extracted by adversaries by retrieving similar images in image retrieval systems. Existing image processing-based privacy-preserving methods suffer from a trade-off of efficacy and usability. We propose a method introducing imperceptible adversarial perturbations on original images to prevent them from being retrieved. Users upload protected adversarial images instead of the original images to preserve privacy while maintaining usability. Then we shift to the security concerns. We act as attackers, proactively providing adversarial images to retrieval systems. These adversarial examples are embedded to specific targets so that the user retrieval results contain our unrelated adversarial images, e.g., users query with a “Husky dog” image, but retrieve adversarial “dog food” images in the result. A transferability-based attack is proposed for black-box models. We improve black-box transferability with the random noise as the proxy in optimization, achieving state-of-the-art success rate. Finally, we stand on retrieval systems side to mitigate the security concerns of adversarial attacks in deep hashing image retrieval. We propose a detection method that detects adversarial examples in the inference time. By studying unique adversarial behaviors in deep hashing image retrieval, our proposed method is constructed on criterions of these adversarial behaviors. The proposed method detects most of the adversarial examples with minimum overhead

BIRP: Software for interactive search and retrieval of image engineering data

Better Image Retrieval Programs (BIRP), a set of programs to interactively sort through and to display a database, such as engineering data for images acquired by spacecraft is described. An overview of the philosophy of BIRP design, the structure of BIRP data files, and examples that illustrate the capabilities of the software are provided

Content based image retrieval using unclean positive examples

Conventional content-based image retrieval (CBIR) schemes employing relevance feedback may suffer from some problems in the practical applications. First, most ordinary users would like to complete their search in a single interaction especially on the web. Second, it is time consuming and difficult to label a lot of negative examples with sufficient variety. Third, ordinary users may introduce some noisy examples into the query. This correspondence explores solutions to a new issue that image retrieval using unclean positive examples. In the proposed scheme, multiple feature distances are combined to obtain image similarity using classification technology. To handle the noisy positive examples, a new two-step strategy is proposed by incorporating the methods of data cleaning and noise tolerant classifier. The extensive experiments carried out on two different real image collections validate the effectiveness of the proposed scheme.<br /

Content Based Image Retrieval Using SVM Algorithm

Conventional content-based image retrieval (CBIR) schemes employing relevance feedback may suffer from some problems in the practical applications. First, most ordinary users would like to complete their search in a single interaction especially on the web. Second, it is time consuming and difficult to label a lot of negative examples with sufficient variety. Third, ordinary users may introduce some noisy examples into the query. This correspondence explores solutions to a new issue that image retrieval using unclean positive examples. In the proposed scheme, multiple feature distances are combined to obtain image similarity using classification technology. To handle the noisy positive examples, a new two step strategy is proposed by incorporating the methods of data cleaning and noise tolerant classifier. The extensive experiments carried out on two different real image collections validate the effectiveness of the proposed scheme

Dynamic Learning of Indexing Concepts for Home Image Retrieval

International audienceThis paper presents a component of a content based image retrieval system dedicated to let a user define the indexing terms used later during retrieval. A user inputs a indexing term name, image examples and counter-examples of the term,and the system learns a model of the concept as well as a similarity measure for this term. The similarity measure is based on weights reflecting the importance of each low-level feature extracted from the images. The system computes these weights using a genetic algorithm. Rating a particular similarity measure is done by clustering the examples and counter-examples using these weights and computing the quality of the obtained clusters. Experiments are conducted and results are presented on a set of 600 images