Biblio

Relevance feedback can be considered as a learning problem. It has been extensively used to improve the performance of retrieval multimedia information. In this paper, after the relevance feedback upon content-based image retrieval (CBIR) discussed, a hybrid learning scheme on multi-target retrieval (MTR) with relevance feedback was proposed. Suppose the symbolic image database (SID) of object-level with combined image metadata and feature model was constructed. During the interactive query for remote sensing image, we calculate the similarity metric so as to get the relevant image sets from the image library. For the purpose of further improvement of the precision of image retrieval, a hybrid learning scheme parameter also need to be chosen. As a result, the idea of our hybrid learning scheme contains an exception maximization algorithm (EMA) used for retrieving the most relevant images from SID and an algorithm called supported vector machine (SVM) with relevance feedback used for learning the feedback information substantially. Experimental results show that our hybrid learning scheme with relevance feedback on MTR can improve the performance and accuracy compared the basic algorithms.

With the emergence of cloud, content-based image retrieval (CBIR) on encrypted domain gain enormous importance due to the ever increasing need for ensuring confidentiality, authentication, integrity and privacy of data. CBIR on outsourced encrypted images can be done by extracting features from unencrypted images and generating searchable encrypted index based on it. Visual descriptors like color descriptors, shape and texture descriptors, etc. are employed for similarity search. Since visual descriptors used to represent an image have crucial role in retrieving most similar results, an attempt to combine them has been made in this paper. The effect of combining different visual descriptors on retrieval precision in secure CBIR scheme proposed by Xia et al. is analyzed. Experimental results show that combining visual descriptors can significantly enhance retrieval precision of the secure CBIR scheme.

Research on Future Internet has gained traction in recent years, with a variety of clean-slate network architectures being proposed. The realization of such proposals may lead to a period of coexistence with the current Internet, creating a heterogeneous Future Internet. In such a vision, mobile nodes (MNs) can move across access networks supporting different network architectures, while being able to maintain the access to content during this movement. In order to support such scenarios, this paper proposes an inter-network architecture mobility framework that allows MNs to move across different network architectures without losing access to the contents being accessed. The usage of the proposed framework is exemplified and evaluated in a mobility scenario targeting IP and NDN network architectures in a content retrieval use case. The obtained results validate the proposed framework while highlighting the impact on the overall communication between the MN and content source.

Trademarks are recognizable images and/or words used to distinguish various products or services. They become associated with the reputation, innovation, quality, and warranty of the products. Countries around the world have offices for industrial/intellectual property (IP) registration. A new trademark image in application for registration should be distinct from all the registered trademarks. Due to the volume of trademark registration applications and the size of the databases containing existing trademarks, it is impossible for humans to make all the comparisons visually. Therefore, technological tools are essential for this task. In this work we use a pre-trained, publicly available Convolutional Neural Network (CNN) VGG19 that was trained on the ImageNet database. We adapted the VGG19 for the trademark image retrieval (TIR) task by fine tuning the network using two different databases. The VGG19v was trained with a database organized with trademark images using visual similarities, and the VGG19c was trained using trademarks organized by using conceptual similarities. The database for the VGG19v was built using trademarks downloaded from the WEB, and organized by visual similarity according to experts from the IP office. The database for the VGG19c was built using trademark images from the United States Patent and Trademarks Office and organized according to the Vienna conceptual protocol. The TIR was assessed using the normalized average rank for a test set from the METU database that has 922,926 trademark images. We computed the normalized average ranks for VGG19v, VGG19c, and for a combination of both networks. Our method achieved significantly better results on the METU database than those published previously.

The goal of content-based recommendation system is to retrieve and rank the list of items that are closest to the query item. Today, almost every e-commerce platform has a recommendation system strategy for products that customers can decide to buy. In this paper we describe our work on creating a Generative Adversarial Network based image retrieval system for e-commerce platforms to retrieve best similar images for a given product image specifically for shoes. We compare state-of-the-art solutions and provide results for the proposed deep learning network on a standard data set.

Cross-modal hashing, which searches nearest neighbors across different modalities in the Hamming space, has become a popular technique to overcome the storage and computation barrier in multimedia retrieval recently. Although dozens of cross-modal hashing algorithms are proposed to yield compact binary code representation, applying exhaustive search in a large-scale dataset is impractical for the real-time purpose, and the Hamming distance computation suffers inaccurate results. In this paper, we propose a novel index scheme over binary hash codes in cross-modal retrieval. The proposed indexing scheme exploits a few binary bits of the hash code as the index code. Based on the index code representation, we construct an inverted index structure to accelerate the retrieval efficiency and train a neural network to improve the indexing accuracy. Experiments are performed on two benchmark datasets for retrieval across image and text modalities, where hash codes are generated by three cross-modal hashing methods. Results show the proposed method effectively boosts the performance over the benchmark datasets and hash methods.

Image retrieval systems have been an active area of research for more than thirty years progressively producing improved algorithms that improve performance metrics, operate in different domains, take advantage of different features extracted from the images to be retrieved, and have different desirable invariance properties. With the ever-growing visual databases of images and videos produced by a myriad of devices comes the challenge of selecting effective features and performing fast retrieval on such databases. In this paper, we incorporate Fourier descriptors (FD) along with a metric-based balanced indexing tree as a viable solution to DHS (Department of Homeland Security) needs to for quick identification and retrieval of weapon images. The FDs allow a simple but effective outline feature representation of an object, while the M-tree provide a dynamic, fast, and balanced search over such features. Motivated by looking for applications of interest to DHS, we have created a basic guns and rifles databases that can be used to identify weapons in images and videos extracted from media sources. Our simulations show excellent performance in both representation and fast retrieval speed.