With the rapid development of remote-sensing earth observation technology, hyperspectral imagery has shown exponential growth. The quick and accurate retrieval of hyperspectral images has become a practical challenge in applications. Moreover, open network sharing has rendered network information security increasingly important. It is necessary to prevent breach of confidentiality events during retrieval, particularly for hyperspectral images containing crucial information. Therefore, a method for hyperspectral image secure retrieval based on encrypted deep spectral–spatial features is proposed. In principle, our method includes the following steps: (1) Considering the powerful feature learning capability of deep networks, deep spectral–spatial features of hyperspectral image are extracted with a deep convolutional generative adversarial network. (2) For high-dimensional deep features, t-distributed Stochastic neighbor embedding based nonlinear manifold hashing is utilized to reduce the dimensionality of deep spectral–spatial features. (3) To ensure data security during retrieval, deep spectral–spatial features are encrypted with feature randomization encryption. (4) Multi-index hashing is utilized to measure similarities among the deep spatial–spectral features of hyperspectral images. (5) Relevance feedback based on feature reweighting is introduced to further improve retrieval accuracy. Four experiments are conducted to prove the effectiveness of the proposed method based on retrieval and security performance. Our experimental results on two hyperspectral datasets show that our method can effectively protect the security of image content with sufficient image retrieval accuracy.
The objective of large-scale object retrieval systems is to search for images that contain the target object in an image database. Where state-of-the-art approaches rely on global image representations to conduct searches, we consider many boxes per image as candidates to search locally in a picture. In this paper, a feature quantization algorithm called binary quantization is proposed. In binary quantization, a scale-invariant feature transform (SIFT) feature is quantized into a descriptive and discriminative bit-vector, which allows itself to adapt to the classic inverted file structure for box indexing. The inverted file, which stores the bit-vector and box ID where the SIFT feature is located inside, is compact and can be loaded into the main memory for efficient box indexing. We evaluate our approach on available object retrieval datasets. Experimental results demonstrate that the proposed approach is fast and achieves excellent search quality. Therefore, the proposed approach is an improvement over state-of-the-art approaches for object retrieval.
Linear Discriminant Analysis (LDA) has been widely applied in the field of face identification because of its simplicity and efficiency in capturing the most discriminant features. However LDA often fails when facing the change in illumination, pose or small training size. To overcome those difficulties, Principal Component Analysis (PCA), which recover the most descriptive/informative features in the reduced dimension feature space, are often used in preprocessing stage. Although there is a trend of preferring LDA over PCA in classification, it has been found that PCA may perform better than LDA in some cases, especially when the size of the training set is small. To better combine the merits of PCA and LDA, some rule-based parametric combination of PCA and LDA methods have been proposed. However in those methods the optimal parameter setting is not guaranteed and can only be approximated by exhaustive search. In this paper we propose a learning-based framework that can unify PCA and LDA in adaptively finding both discriminant and descriptive feature. To eliminate the parameter selection, we incorporate a non-linear boosting process to enhance a pool of hybrid classifiers and combine them into a more accurate one. To evaluate the performance of our boosted hybrid method, we compare it to state-of-the-art LDA variants and traditional PCA-LDA technique on three widely used face image benchmark databases. The experiment results show that our novel boosted hybrid discriminant analysis outperforms the other techniques and the best single hybrid classifier.
A voting-based object boundary reconstruction approach is proposed in this paper. Morphological technique was
adopted in many applications for video object extraction to reconstruct the missing pixels. However, when the missing
areas become large, the morphological processing cannot bring us good results. Recently, Tensor voting has attracted
people's attention, and it can be used for boundary estimation on curves or irregular trajectories. However, the
complexity of saliency tensor creation limits its applications in real-time systems. An alternative approach based on
tensor voting is introduced in this paper. Rather than creating saliency tensors, we use a "2-pass" method for orientation
estimation. For the first pass, Sobel d*etector is applied on a coarse boundary image to get the gradient map. In the
second pass, each pixel puts decreasing weights based on its gradient information, and the direction with maximum
weights sum is selected as the correct orientation of the pixel. After the orientation map is obtained, pixels begin linking
edges or intersections along their direction. The approach is applied to various video surveillance clips under different
conditions, and the experimental results demonstrate significant improvement on the final extracted objects accuracy.
In Content-based Image Retrieval the comparison of a query image and each of the database images is defined by a similarity distance obtained from the two feature vectors involved. These feature vectors can be seen as sets of noisy indexes. Unlike text matching (that is exact) image matching is only approximate, leading to ranking
methods. Only images at the top ranks (within the scope) are returned as retrieval results. Image retrieval performance characterization has mainly been based on measures available from probabilistic text retrieval in the form of Precision-Recall or Precision-Scope graphs. However, these graphs offer an incomplete overview of the image retrieval system under study. Essential information about how the success of the query is influenced by the size and type of irrelevant images is missing. Due to the inexactness of the visual matching process, the effect of the irrelevant embedding, represented in the additional performance measure generality, plays an important role.
In general, a performance graph will be three-dimensional, a Generality-Recall-Precision Graph. By choosing appropriate scope values a new two-dimensional performance graph, the Generality-Recall-Precision Graph, is proposed to replace the commonly used Precision-Recall Graph, as the better choice for total recall studies.
In content-based image retrieval (CBIR), in order to alleviate learning in the high-dimensional space, Fisher discriminant analysis (FDA) and multiple discriminant analysis (MDA) are commonly used to find an optimal discriminating subspace that the data are clustered in the reduced feature space, in which the probabilistic structure of the data could be simplified and captured by simpler model assumption, e.g., Gaussian mixtures. However, due to the two reasons (i) the real number of clases in the image database is usually unknown; and (ii) the image retrieval system acts as a classifier to divide the images into two classes, relevant and irrelevant, the effective dimension of projected subspace is usually one. In this paper, a novel hybrid feature dimension reduction techniqe is proposed to construct descriptive and discriminant features at the same time by maximizing the Rayleigh coefficient. The hybrid LDA and PCA analysis not only increases the effective dimension of the projected subspace, but also offers more flexibility and alternatives to LDA and PCA. Extensive tests on benchmark and real image databases have shown the superior performances of the hybrid analysis.
In this paper, we present a general guideline to establish the relation of noise distribution model and its corresponding error metric. By designing error metrics, we obtain a much richer set of distance measures besides the conventional Euclidean distance or SSD (sum of the squared difference) and the Manhattan distance or SAD (sum of the absolute difference). The corresponding nonlinear estimations such as harmonic mean, geometric mean, as well as their generalized nonlinear operations are derived. It not only offers more flexibility than the conventional metrics but also discloses the coherent relation between the noise model and its corresponding error metric. We experiment with different error metrics for similarity noise estimation and compute the accuracy of different methods in three kinds of applications: content-based image retrieval from a large database, stereo matching, and motion tracking in video sequences. In all the experiments, robust results are obtained for noise estimation based on the proposed error metric analysis.
Content-based image retrieval has become one of the most active research areas in the past few years. Most of the attention from the research has been focused on indexing techniques based on global feature distributions. However, these global distributions have limited discriminating power because they are unable to capture local image information. Applying global Gabor texture features greatly improves the retrieval accuracy. But they are computationally complex. In this paper, we present a wavelet-based salient point extraction algorithm. We show that extracting the color and texture information in the locations given by these points provides significantly improved results in terms of retrieval accuracy, computational complexity and storage space of feature vectors as compared to the global feature approaches.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.