We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech based label annotations. Notice that these multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the growing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network based architecture which is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multi-spectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases.
The image retrieval frameworks can either be content-based or text-based, depending upon the nature of the query data. The applications of CBIR in RS mainly gained its motion after the Landsat thematic mapper multi-spectral satellite data were made accessible. Subsequently, several techniques came up which focus on training of the retrieval models through the notion of active learning by incorporating the user's knowledge during training iterations @cite_13 . As opposed to the real-valued feature representations, several techniques consider the binary features for quicker response during retrieval. In this regard, @cite_12 proposes a hashing-based approximate nearest neighbor search for a fast and scalable RS image retrieval. Visual databases targeted by CBIR applications have been extended for multi-label image retrieval @cite_7 @cite_6 .
@cite_13 @cite_7 @cite_12 @cite_6
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Given the growing amount of very large image databases, content based image retrieval (CBIR) is becoming more and more important. One of the major challenges in CBIR is the semantic gap - commonly used feature-based algorithms are not able to identify what really draws human attention in an image. This problem is more crucial for localized CBIR, where certain regions parts of the image are what the user is really interested in. This paper explores how human gaze can be utilized to extract regions of interest (ROIs) of an image to perform attention based image retrieval. Using eye tracking data and knowledge about foveal and peripheral vision of humans, we present a foveal fixation clustering algorithm that automatically generates ROIs in an image while a person is viewing it. To objectively set different parameters of the algorithm, a small user study was conducted. The method was evaluated for use in a localized CBIR system. Image retrieval results using the publicly available SIVAL dataset were scored using mean average precision (MAP). Comparison to a saliency-based visual attention algorithm as well as to manually labeled regions showed that the retrieval results of the developed algorithm are nearly two times better than the saliency-based visual attention algorithm and very close to the results using hand-labeled regions. Content-based image retrieval (CBIR) has been more and more important in the last decade, and the gap between high-level semantic concepts and low-level visual features hinders further performance improvement. The problem of online feature selection is critical to really bridge this gap. In this paper, we investigate online feature selection in the relevance feedback learning process to improve the retrieval performance of the region-based image retrieval system. Our contributions are mainly in three areas. 1) A novel feature selection criterion is proposed, which is based on the psychological similarity between the positive and negative training sets. 2) An effective online feature selection algorithm is implemented in a boosting manner to select the most representative features for the current query concept and combine classifiers constructed over the selected features to retrieve images. 3) To apply the proposed feature selection method in region-based image retrieval systems, we propose a novel region-based representation to describe images in a uniform feature space with real-valued fuzzy features. Our system is suitable for online relevance feedback learning in CBIR by meeting the three requirements: learning with small size training set, the intrinsic asymmetry property of training samples, and the fast response requirement. Extensive experiments, including comparisons with many state-of-the-arts, show the effectiveness of our algorithm in improving the retrieval performance and saving the processing time. A major challenge in Content-Based Image Retrieval (CBIR) is to bridge the semantic gap between low-level image contents and high-level semantic concepts. Although researchers have investigated a variety of retrieval techniques using different types of features and distance functions, no single best retrieval solution can fully tackle this challenge. In a real-world CBIR task, it is often highly desired to combine multiple types of different feature representations and diverse distance measures in order to close the semantic gap. In this paper, we investigate a new framework of learning to rank for CBIR, which aims to seek the optimal combination of different retrieval schemes by learning from large-scale training data in CBIR. We first formulate the problem formally as a learning to rank task, which can be solved in general by applying the existing batch learning to rank algorithms from text information retrieval (IR). To further address the scalability towards large-scale online CBIR applications, we present a family of online learning to rank algorithms, which are significantly more efficient and scalable than classical batch algorithms for large-scale online CBIR. Finally, we conduct an extensive set of experiments, in which encouraging results show that our technique is effective, scalable and promising for large-scale CBIR. The retrieval facilities of most peer-to-peer (P2P) systems are limited to queries based on a unique identifier or a small set of keywords. The techniques used for this purpose are hardly applicable for content based image retrieval (CBIR) in a P2P network. Furthermore, we will argue that the curse of dimensionality and the high communication overhead prevent the adaptation of multidimensional search trees or fast sequential scan techniques for P2P CBIR. In the present paper we will propose two compact data representations that can be distributed in a P2P network and used as the basis for a source selection. This allows for communicating with only a small fraction of all peers during query processing without deteriorating the result quality significantly. We will also present experimental results confirming our approach.