Synthetic aperture radar (SAR) is an advanced active microwave sensor widely used in marine surveillance. As part of typical marine surveillance missions, ship classification in synthetic aperture radar (SAR) images is a significant task for the remote sensing community. However, fully utilizing polarization information to enhance SAR ship classification remains an unresolved issue. Thus, we proposed a dual-polarization information-guided network (DPIG-Net) to solve it. DPIG-Net utilizes available dual-polarization information from the Sentinel-1 SAR satellite to adaptively guide feature extraction and feature fusion. We first designed a novel polarization channel cross-attention framework (PCCAF) to model the correlations of different polarization information for feature extraction. Then, we established a novel dilated residual dense learning framework (DRDLF) to refine the polarization characteristics for feature fusion. The results on the open OpenSARShip dataset indicated DPIG-Net’s state-of-the-art classification accuracy compared with eleven other competitive models, which showed the potential of DPIG-Net to promote effective and sufficient utilization of SAR polarization data in the future. 

/pdf/a-dual-polarization-information-guided-network-for-sar-ship-1rf0og6t.pdf

A Dual-Polarization Information-Guided Network for SAR Ship Classification

As an advanced research topic in forensics science, automatic shoe-print identification has been extensively studied in the last two decades, since shoe marks are the clues most frequently left in a crime scene. Hence, these impressions provide a pertinent evidence for the proper progress of investigations in order to identify the potential criminals. The main goal of this survey is to provide a cohesive overview of the research carried out in forensic shoe-print identification and its basic background. Apart defining the problem and describing the phases that typically compose the processing chain of shoe-print identification, we provide a summary/comparison of the state-of-the-art approaches, in order to guide the neophyte and help to advance the research topic. This is done through introducing simple and basic taxonomies as well as summaries of the state-of-the-art performance. Lastly, we discuss the current open problems and challenges in this research topic, point out for promising directions in this field.

Forensic Shoe-print Identification: A Brief Survey.

Crime scene investigation is an important and challenging work for detecting suspects from the incident. Investigations begin from collecting various objects, markings, location, and scalability of the incident. Evaluation of artificial intelligence helped a lot in creating automated investigation models. To detect the crime scene objects, markings impact a lot in making decisions. Investigation scenario is highly sensitive; hence, detection of crime scene objects as early as possible is important. The proposed approach considers crime scene videos collected from CCTV cameras as prime input. Video-to-image conversion is implemented initially. Visual HOG histogram of orientation gradient feature (VHOG) is extracted from the image. Based on the feature, the background subtraction is done. The semantic object is extracted from the image through morphology factor as well as HOG feature matching. The correlated semantic object shades are compared with the training images. Deep resilient net (DRN) is created to make training and testing processes. Various images of the objects are separately trained using the neural network. Using the hidden layers of neural perceptrons, the similar blob of the object is continuously compared with all the salient objects in the database images. Based on the correlated score, the confusion matrix is formulated. The calculation of true positive, true negative, false positive, and false negative rate is evaluated. The novel structure is validated with repeated iterations of comparisons and further the achieved the accuracy of 91%. Reduction of false similarity issues is considered here to avoid occluded images, and rejection of false image is adopted steps here. 

Visual HOG-Enabled Deep ResiNet for Crime Scene Object Detection

With proliferation of social media, image has become ubiquitous giving rise to the demand and importance of image semantic analysis and retrieval to access information quickly on social media. However, even with humongous information available, there are certain categories of images which are important for certain applications but are very scarce. Convolutional neural network is an effective method to extract high-level semantic features for image database retrieval. To overcome the problem of over-fitting when the number of training samples in dataset is limited, this paper proposes an image database retrieval algorithm based on the framework of transfer learning and feature fusion. Based on the fine-tuning of the pre-trained Convolutional Neural Network (CNN), the proposed algorithm first extracts the semantic features of the images. Principal Component Analysis (PCA) is then applied for dimension reduction and to reduce the computational complexity. Last, the semantic feature extracted from the CNN is fused with traditional low-level visual feature to improve the retrieval accuracy further. Experimental results demonstrated the effectiveness of the proposed method for image database retrieval.

A novel image retrieval algorithm based on transfer learning and fusion features

Crime scene investigation (CSI) image retrieval is used to search for crime evidences and is critical in helping in solving various crimes. In recent years, using Convolutional Neural Network (CNN) has demonstrated outstanding performances in large-scale image database retrieval. However, to prevent over-fitting in the training of CNN model due to limited number of CSI images, this paper proposes to cascade two CNN models obtained based on transfer learning and combine CNN features with low-level image feature to better describe CSI images. First, two pre-trained CNN models are fine-tuned using the target image set. CNN features are extracted from fully connected layer of each model and are concatenated as high-level features for the image. These concatenated CNN features are then fused with the low-level image features of the target image set. The final fused image features are used in the image retrieval. Experimental results on CSI image database proved the effectiveness of the proposed algorithm for limited number of training sets. In addition, experiments carried out on the GHIM-10K database proved the generalizability of the proposed algorithm.

Image Retrieval using CNN and Low-level Feature Fusion for Crime Scene Investigation Image Database

Based on a large scale crime scene investigation (CSI) image database, an effective and efficient CSI image retrieval system has been proposed to empower the investigative work of the police force. The main contribution of this paper includes: (1) a DCT domain texture feature extraction algorithm is proposed for CSI images, which is shown to be simple and effective. (2) the use of GIST descriptor on CSI images for the first time and combined with color histogram and the DCT domain texture feature as a fused feature, which describes CSI images from different aspects including color, texture, and scene content. Experimental results prove that the proposed method is effective for CSI image retrieval.

Multi-Feature Fusion for Crime Scene Investigation Image Retrieval

Crime scene investigation (CSI) images are important clues for the police to solve cases. With the available of large scale CSI image database, effective and efficient CSI image retrieval becomes more and more important. The main contribution of this paper includes: (1) a DCT domain texture feature extraction algorithm is constructed for effective description of CSI images; (2) GIST descriptor is first exploited in the description of CSI images and integrated with color histogram and the DCT domain texture feature as a fused feature, which describes CSI images from different view including color, texture, and scene structure; (3) SVM Classification technique is used in CSI image retrieval. Experimental results on real CSI image data show that the fusion feature proposed in this paper can well describe the content of CSI images, with an average 15.3% increment in retrieval precision compared with all the single-feature-based algorithm. Using this fusion feature to further train the SVM classifier in the retrieval process, the precision is further improved by 3.1%.

Multi-feature fusion with SVM classification for crime scene investigation image retrieval

The present invention relates to a scene investigation image retrieval method based on low-level image features and CNN features, the method mainly utilizing image features extracted from a CNN modeland enabling fusion of the image features based on a CNN and traditional image features to solve the problem of describing the effective features of database images with complex contents and eliminating the semantic gap. In order to further improve the retrieval efficiency, the similarity of images is calculated by using the middle-level feature of a convolutional neural network and the low-levelfeature vector of traditional images, and the final image similarity sim is calculated. The similarity between images is measured by the distance between blocks of image feature vectors or feature matrices. Compared with a conventional low-level feature retrieval method, the method improves the retrieval accuracy to a considerable extent through experimental verification.

Scene investigation image retrieval method based on low-level image features and CNN features

Video surveillance contains a lot of facial occlusion, which brings great difficulties to the detection of criminal investigation cases. Current face inpainting algorithms are difficult to meet the uniqueness requirements of face comparison, due to the lack of a priori information within the occluded area. Face sketch drawn by experienced simulated portrait artist according to low-quality video or description of the victim contains lots of useful information. There, this paper proposes a face inpainting algorithm combining face sketch and gate convolution. First, the face sketch, used as guided information, integrates into the occluded face image to complete the missing area. Then, a generative adversarial networks (GAN) with gate convolution is designed for model training, which effectively suppresses the interference of the occlusion area to the inpainting process. The experimental results show that the proposed algorithm obtain the better inpainting results and larger SSIM compared with the other algorithm. The proposed obtain better comprehensive performance.

Face Inpainting Algorithm Combining Face Sketch and Gate Convolution

Aiming at the problem that guided filtering would cause halo near the edge and it is difficult to identify fine edges, a guided filtering combining neighborhood variance and anisotropic window is proposed. Firstly, the directional selectivity of the anisotropic Gaussian filter is used for the recognition of the fine edge, and the narrow spatial structure of the filter can benefit the information fusion of different pixels inside the local window to suppress the edge blur and halo effect. Secondly, based on the local structure similarity, the neighborhood variance is introduced to optimize the parameters of local linear transformation, so as to achieve the maximum diffusion at non-edge region while preserving strong edges. The experimental results on 102 Category Flower Dataset show that, compared with other edge-preserving filtering methods, the proposed method is superior to other methods in visual effect and quantitative evaluation (PSNR and SSIM), and the distortion of the test image is 46.72%, 48.64% and 29.61% smaller than guided filtering, weighted guided filtering and anisotropic guided filtering respectively. It can effectively suppress the occurrence of artifacts while recognizing precise edges. 

Guided Filtering Combining Neighborhood Variance and Anisotropic Window

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