Contextual Image Illustrator: A Review

Any story or any other literary content is best understood and advertised with the help of pictures. Images are used to arouse reader’s interest and comprehension in the content. The contextual image illustrator will take any content description and will output the ranked images related to that content. The text can be any blog, newspaper article, any story or any other content. The image retrieval process that has been used for this purpose is Text based Image Retrieval, i.e., TBIR. Semantic keywords are extricated from the story; images are looked through an annotated database. Thereafter, an image ranking scheme will determine the relevance of each image. Then the user can choose among the images displayed. A score along with each image will also be displayed representing its relevance to the query. The keywords stemming and stop word removal has been explained in the document. Also, the algorithm that has been designed to determine the score and hence the image’s significance has been calculated. Testing consisting of both unit testing and module testing of the project are explained. Keywords: Keyword Extraction, Image Search, Stemming, Stop word Removal, URL Score, URL Ranking

Evaluating a Novel P300-Based Real-Time Image Ranking BCI

Brain–computer interfaces (BCIs) establish communication between a human brain and a computer or external devices by translating the electroencephalography (EEG) signal into computer commands. After stimulating a sensory organ, a positive deflection of the EEG signal between 250 and 700 ms can be measured. This signal component of the event-related potential (ERP) is called “P300.” Numerous studies have provided evidence that the P300 amplitude and latency are linked to sensory perception, engagement, and cognition. Combining the advances in technology, classification methods, and signal processing, we developed a novel image ranking system called the Unicorn Blondy Check. In this study, the application was tested on 21 subjects using three different visual oddball paradigms. Two consisted of female faces and gray-scale images, while the third test paradigm consisted of familiar and unfamiliar faces. The images were displayed for a duration of 150 ms in a randomized order. The system was trained using 50 trials and tested with 30 trials. The EEG data were acquired using the Unicorn Hybrid Black eight-channel BCI system. These synchronized recordings were analyzed, and the achieved classification accuracies were calculated. The EEG signal was averaged over all participants and for every paradigm separately. Analysis of the EEG data revealed a significant shift in the P300 latency dependent on the paradigm and decreased amplitude for a lower target to non-target ratio. The image ranking application achieved a mean accuracy of 100 and 95.5% for ranking female faces above gray-scale images with ratios of 1:11 and 5:11, respectively. In the case of four familiar faces to 24 unfamiliar faces, 86.4% was reached. The obtained results illustrate this novel system’s functionality due to accuracies above chance levels for all subjects.

A human-intuitive fuzzy ranking approach with spread and skewness factors

It would be hard to deny the importance of fuzzy number ranking in fuzzy-based applications. The definition of fuzzy ranking, however, evades an easy description due to the overlapping of fuzzy sets. While many researchers have addressed this subject, close examination reveals that their results suffer from one or more shortcomings such as image-ranking problems or ranking two equally embedded fuzzy numbers with the same centroid and different spreads. This paper proposes a new fast and straightforward computational approach to ranking fuzzy numbers that aims to overcome such problems. The proposed approach considers several important factors such as spread, skewness and center, in addition to human intuition. Further, the proposed ranking approach involves a composition of these factors as demonstrated in the several examples provided and in comparison with other existing approaches.

Online social image ranking in diversified preferences

Due to the prevalence of social media service, effective and efficient online image retrieval is in urgent need to satisfy diversified requirements of Web users. Previous studies are mainly focusing on bridging the semantic gap by well-established content modeling with semantic information and social tagging information, but they are not flexible in aggregating the diversified expectations of the online users. In this paper, we present OSIR, a solution framework to facilitate the diversified preference styles in online social media image searching by textual query inputs. First, we propose an efficient Online Multiple Kernel Ranking (OMKR) model which is constructed on multiple query dimensions and complimentary feature channels, and trained by minimizing the triplet loss on hard negative samples. By optimizing the ranking performance with multi-dimensional queries, the semantic consistency between the image ranking and textual query input is directly maximized without relying on the intermediate semantic annotation procedure. Second, we construct random walk-based preference modeling by domain-specific similarity calculation on heterogeneous social attributes. By re-ranking the rank output of OMKR based on each preference ranking model, we obtain a set of ranking lists encoding different potential aspects of user preference. Last, we propose an effective and efficient position-sensitive rank aggregation approach to aggregate multiple ranking results based on the user preference specification. Extensive experiment on two social media datasets demonstrates the advantages of our approach in both retrieval performance and user experience.

Convolutional neural networks for improving image quality with noisy PET data

Goal PET is a relatively noisy process compared to other imaging modalities, and sparsity of acquisition data leads to noise in the images. Recent work has focused on machine learning techniques to improve PET images, and this study investigates a deep learning approach to improve the quality of reconstructed image volumes through denoising by a 3D convolution neural network. Potential improvements were evaluated within a clinical context by physician performance in a reading task. Methods A wide range of controlled noise levels was emulated from a set of chest PET data in patients with lung cancer, and a convolutional neural network was trained to denoise the reconstructed images using the full-count reconstructions as the ground truth. The benefits, over conventional Gaussian smoothing, were quantified across all noise levels by observer performance in an image ranking and lesion detection task. Results The CNN-denoised images were generally ranked by the physicians equal to or better than the Gaussian-smoothed images for all count levels, with the largest effects observed in the lowest-count image sets. For the CNN-denoised images, overall lesion contrast recovery was 60% and 90% at the 1 and 20 million count levels, respectively. Notwithstanding the reduced lesion contrast recovery in noisy data, the CNN-denoised images also yielded better lesion detectability in low count levels. For example, at 1 million true counts, the average true positive detection rate was around 40% for the CNN-denoised images and 30% for the smoothed images. Conclusion Significant improvements were found for CNN-denoising for very noisy images, and to some degree for all noise levels. The technique presented here offered however limited benefit for detection performance for images at the count levels routinely encountered in the clinic.