Automatic Query Image Disambiguation for Content-Based Image Retrieval

Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user. We propose a technique for overcoming this ambiguity, while keeping the amount of required user interaction at a minimum. To achieve this, the neighborhood of the query image is divided into coherent clusters from which the user may choose the relevant ones. A novel feedback integration technique is then employed to re-rank the entire database with regard to both the user feedback and the original query. We evaluate our approach on the publicly available MIRFLICKR-25K dataset, where it leads to a relative improvement of average precision by 23% over the baseline retrieval, which does not distinguish between different image senses.Comment: VISAPP 2018 paper, 8 pages, 5 figures. Source code: https://github.com/cvjena/ai

A content-based image retrieval system for outdoor ecology learning: a firefly watching system

[[abstract]]We devote to provide teachers and students with short-range wireless learning environment. The wireless learning platform consists of wireless handheld devices (PDA, notebook, etc.) carried by the guide and learners. A content-based image retrieval system (CBIR) is constructed to provide learner with required information using image recognition and wireless transmission technologies, such that the objective of outdoor ecology learning can be achieved. A firefly database is used as an instance to illustrate the operations of CBIR system. Instead of learning from textbook, a real firefly in natural environment can be observed and learned through digital camera and image recognition system. During the learning activity, the teacher can use this CBIR system to control the learning progress, evaluate the learning effects and provide necessary assistances to students in order to have a flourish learning environment.[[conferencetype]]國際[[conferencedate]]20040329~20040329[[conferencelocation]]Fukuoka, Japa

Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval

Relevance feedback schemes based on support vector machines (SVM) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based relevance feedback is often poor when the number of labeled positive feedback samples is small. This is mainly due to three reasons: 1) an SVM classifier is unstable on a small-sized training set, 2) SVM's optimal hyperplane may be biased when the positive feedback samples are much less than the negative feedback samples, and 3) overfitting happens because the number of feature dimensions is much higher than the size of the training set. In this paper, we develop a mechanism to overcome these problems. To address the first two problems, we propose an asymmetric bagging-based SVM (AB-SVM). For the third problem, we combine the random subspace method and SVM for relevance feedback, which is named random subspace SVM (RS-SVM). Finally, by integrating AB-SVM and RS-SVM, an asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve these three problems and further improve the relevance feedback performance

ВЛИЯНИЕ МЕР БЛИЗОСТИ В ПРОСТРАНСТВЕ ПРИЗНАКОВ НА КАЧЕСТВО ПОИСКА МЕДИЦИНСКИХ ИЗОБРАЖЕНИЙ ПО СОДЕРЖАНИЮ

Проводится экспериментальное исследование влияния используемых мер близости в пространстве признаков на качество поиска медицинских изображений. Приводятся результаты сравнения 16 наиболее распространенных метрик при решении задачи поиска рентгеновских изображений по образцу на основе тестовой базы данных, состоящей из 3000 изображений грудной клетки испытуемых обоего пола в возрасте 20, 40 и 60 лет. Показано, что выбор метрики существенным образом влияет на качество результатов поиска, а наилучшие результаты достигаются при вычислении степени близости с использованием расстояния Kullback – Leibler

Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings

Advances in object recognition flourished in part because of the availability of high-quality datasets and associated benchmarks. However, these benchmarks---such as ILSVRC---are relatively task-specific, focusing predominately on predicting class labels. We introduce a publicly-available dataset that embodies the task-general capabilities of human perception and reasoning. The Human Similarity Judgments extension to ImageNet (ImageNet-HSJ) is composed of human similarity judgments that supplement the ILSVRC validation set. The new dataset supports a range of task and performance metrics, including the evaluation of unsupervised learning algorithms. We demonstrate two methods of assessment: using the similarity judgments directly and using a psychological embedding trained on the similarity judgments. This embedding space contains an order of magnitude more points (i.e., images) than previous efforts based on human judgments. Scaling to the full 50,000 image set was made possible through a selective sampling process that used variational Bayesian inference and model ensembles to sample aspects of the embedding space that were most uncertain. This methodological innovation not only enables scaling, but should also improve the quality of solutions by focusing sampling where it is needed. To demonstrate the utility of ImageNet-HSJ, we used the similarity ratings and the embedding space to evaluate how well several popular models conform to human similarity judgments. One finding is that more complex models that perform better on task-specific benchmarks do not better conform to human semantic judgments. In addition to the human similarity judgments, pre-trained psychological embeddings and code for inferring variational embeddings are made publicly available. Collectively, ImageNet-HSJ assets support the appraisal of internal representations and the development of more human-like models