This paper describes PinView, a content-based image retrieval system that exploits implicit relevance feedback collected during a search session. PinView contains several novel methods to infer the intent of the user. From relevance feedback, such as eye movements or pointer clicks, and visual features of images, PinView learns a similarity metric between images which depends on the current interests of the user. It then retrieves images with a specialized online learning algorithm that balances the tradeoff between exploring new images and exploiting the already inferred interests of the user. We have integrated PinView to the content-based image retrieval system PicSOM, which enables applying PinView to real-world image databases. With the new algorithms PinView outperforms the original PicSOM, and in online experiments with real users the combination of implicit and explicit feedback gives the best results.
Later, also @cite_36 and @cite_14 @cite_60 and @cite_44 introduced their image retrieval systems using eye movements. The first one @cite_36 was based on a conceptual interface designed to be controlled completely by implicit gaze, providing a mix of a browsing and search tool. A small-scale online experiment was provided, but it cannot be used for drawing strong conclusions on the accuracy of the retrieval results. The second study mostly concentrated on the accuracy of inferring the relevance in @cite_60 and on fixation-weighted region matching between the query and database images in @cite_14 . The last one @cite_44 used gaze data for genuinely implicit relevance feedback by the means of reranking the results of Google Image Search. However, the system was not fully functional yet as the described experimantal evaluation was done in a non-interactive mode.
@cite_36 @cite_44 @cite_14 @cite_60
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We introduce GaZIR, a gaze-based interface for browsing and searching for images. The system computes on-line predictions of relevance of images based on implicit feedback, and when the user zooms in, the images predicted to be the most relevant are brought out. The key novelty is that the relevance feedback is inferred from implicit cues obtained in real-time from the gaze pattern, using an estimator learned during a separate training phase. The natural zooming interface can be connected to any content-based information retrieval engine operating on user feedback. We show with experiments on one engine that there is sufficient amount of information in the gaze patterns to make the estimated relevance feedback a viable choice to complement or even replace explicit feedback by pointing-and-clicking. In this paper we propose an implicit relevance feedback method with the aim to improve the performance of known Content Based Image Retrieval (CBIR) systems by re-ranking the retrieved images according to users' eye gaze data. This represents a new mechanism for implicit relevance feedback, in fact usually the sources taken into account for image retrieval are based on the natural behavior of the user in his her environment estimated by analyzing mouse and keyboard interactions. In detail, after the retrieval of the images by querying CBIRs with a keyword, our system computes the most salient regions (where users look with a greater interest) of the retrieved images by gathering data from an unobtrusive eye tracker, such as Tobii T60. According to the features, in terms of color, texture, of these relevant regions our system is able to re-rank the images, initially, retrieved by the CBIR. Performance evaluation, carried out on a set of 30 users by using Google Images and "pyramid" like keyword, shows that about the 87 of the users is more satisfied of the output images when the re-raking is applied. Image retrieval technology has been developed for more than twenty years. However, the current image retrieval techniques cannot achieve a satisfactory recall and precision. To improve the effectiveness and efficiency of an image retrieval system, a novel content-based image retrieval method with a combination of image segmentation and eye tracking data is proposed in this paper. In the method, eye tracking data is collected by a non-intrusive table mounted eye tracker at a sampling rate of 120 Hz, and the corresponding fixation data is used to locate the human's Regions of Interest (hROIs) on the segmentation result from the JSEG algorithm. The hROIs are treated as important informative segments objects and used in the image matching. In addition, the relative gaze duration of each hROI is used to weigh the similarity measure for image retrieval. The similarity measure proposed in this paper is based on a retrieval strategy emphasizing the most important regions. Experiments on 7346 Hemera color images annotated manually show that the retrieval results from our proposed approach compare favorably with conventional content-based image retrieval methods, especially when the important regions are difficult to be located based on visual features. Relevance feedback (RF) mechanisms are widely adopted in Content-Based Image Retrieval (CBIR) systems to improve image retrieval performance. However, there exist some intrinsic problems: (1) the semantic gap between high-level concepts and low-level features and (2) the subjectivity of human perception of visual contents. The primary focus of this paper is to evaluate the possibility of inferring the relevance of images based on eye movement data. In total, 882 images from 101 categories are viewed by 10 subjects to test the usefulness of implicit RF, where the relevance of each image is known beforehand. A set of measures based on fixations are thoroughly evaluated which include fixation duration, fixation count, and the number of revisits. Finally, the paper proposes a decision tree to predict the user's input during the image searching tasks. The prediction precision of the decision tree is over 87 , which spreads light on a promising integration of natural eye movement into CBIR systems in the future.