Adaptive image segmentation for region-based object retrieval using generalized Hough transform

Query by image content is a method to retrieve the most important images from the image database. It is an answer for the problem of searching for digital images in large database. A large number of relevance feedback schemes have been developed to improve the performance of content based image retrieval. In this paper we propose biased discriminant Euclidean embedding that form intraclass geometry and interclass discrimination. In this method images can be grouped by their similarity. In order to achieve this, firstly the visual features of the images are found out. In addition to this we use two clustering algorithms to assemble the images into clusters using their low level visual features. Here we have to filter the images in the hierarchical clustering and then apply the clustered images to K-Means, so that we can get the most relevant images. Query image is compared directly with the images in the clusters. Therefore number of comparisons is reduced because query image is compared only with the images in the clusters instead of comparing with all images in the database.

Illumination distortion due to uncontrolled lighting can severely degrade the color appearance of a photo. Frequently, the desired colors for objects in a newly taken query image are found in a previously stored database image. Then, the goal is to change the colors in the query image to match the colors in the database image. This paper presents a color restoration system that automatically retrieves a database image which matches the query image, even if the two images are taken from different viewpoints and under different illuminations. Robust features enable both accurate retrieval from the database and efficient sampling of the color differences between the query and database images. A spatially varying color mismatch model is generated, and the colors of the query image are effectively restored.

One of the techniques in region based image retrieval (RBIR) is comparing the global feature of an entire image and the local feature of image’s sub-block in query and database image. The determined sub-block must be able to detect an object with varying sizes and locations. So the sub-block with flexible size and location is needed. We propose a new method for local feature extraction by determining the flexible size and location of sub-block based on the transition region in region based image retrieval. Global features of both query and database image are extracted using invariant moment. Local features in database and query image are extracted using hue, saturation, and value (HSV) histogram and local binary patterns (LBP). There are several steps to extract the local feature of sub-block in the query image. First, preprocessing is conducted to get the transition region, then the flexible sub-block is determined based on the transition region. Afterward, the local feature of sub-block is extracted. The result of this application is the retrieved images ordered by the most similar to the query image. The local feature extraction with the proposed method is effective for image retrieval with precision and recall value are 57%.

Any city-scale visual localization system has to overcome long-term appearance changes, such as varying illumination conditions or seasonal changes between query and database images. Since semantic content is more robust to such changes, we exploit semantic information to improve visual localization. In our scenario, the database consists of gnomonic views generated from panoramic images (e.g. Google Street View) and query images are collected with a standard field-of-view camera at a different time. To improve localization, we check the semantic similarity between query and database images, which is not trivial since the position and viewpoint of the cameras do not exactly match. To learn similarity, we propose training a CNN in a self-supervised fashion with contrastive learning on a dataset of semantically segmented images. With experiments we showed that this semantic similarity estimation approach works better than measuring the similarity at pixel-level. Finally, we used the semantic similarity scores to verify the retrievals obtained by a state-of-the-art visual localization method and observed that contrastive learning-based pose verification increases top-1 recall value to 0.90 which corresponds to a 2% improvement.

Segmentation of intracranial aneurysm images acquired using magnetic resonance angiography (MRA) is essential for medical auxiliary treatments, which can effectively prevent subarachnoid hemorrhages. This paper proposes an image segmentation model based on a dense convolutional attention U-Net, which fuses deep and rich semantic information with shallow-detail information for adaptive and accurate segmentation of MRA-acquired aneurysm images with large size differences. The U-Net model serves as a backbone, combining dense block and convolution block attention module (CBAM). The dense block is composed of a batch normalization layer, an randomly rectified linear unit activation function, and a convolutional layer, for mitigation of vanishing gradients, for multiplexing of aneurysm features, and for improving the network training efficiency. The CBAM is composed of a channel attention module and a spatial attention module, improving the segmentation performance of feature discrimination and enhancing the acquisition of key feature information. Owing to the large variation of aneurysm sizes, multi-scale fusion is performed during up-sampling, for adaptive segmentation of MRA-acquired aneurysm images. The model was tested on the MICCAI 2020 ADAM dataset, and its generalizability was validated on the clinical aneurysm dataset (aneurysm sizes: < 3 mm, 3–7 mm, and > 7 mm) supplied by the Affiliated Hospital of Qingdao University. A good clinical application segmentation performance was demonstrated.

Reliable transmission of high quality images through wireless communication channels represents a great challenge, especially for real-time applications. From one aspect, it is necessary to achieve efficient compression that maintains the good image quality without increasing the transmission bandwidth. Secondly, wireless channels impairments such as fading and interference increase the probability of errors when the image data is transmitted. This necessitates the use of efficient channel coding to protect against channel errors. However channel coding results in increasing the transmitted bit rate, for real time applications. Again, this is constrained by the usually limited available transmission bandwidth. In this paper, this dilemma is solved by adopting new unequal error protection techniques that are used to achieve the above requirements by using image adaptive combined compression and error protection. This is achieved through image segmentation and adaptive compression and channel coding according to the importance of the image segments. Two proposed techniques are introduced. In the first suggested technique, the image is partitioned into equal blocks and the data classification is performed in the DCT domain. The DCT coefficients of the different blocks are classified to different classes of importance and hence they are given different rates of error protection and compression ratios. The second technique is based on adaptive image segmentation using quadtree decomposition into unequal segments before applying the DCT. Finally, to improve the quality of received images, the Orientation Adaptive Sequential Interpolation error-concealment technique is applied to the reconstructed images. Computer simulation shows that the proposed techniques achieve high degree of robustness against channel impairments with good image quality with acceptable data rates.

In content-based image retrieval (CBIR), a set of low-level features are extracted from an image to represent its visual content. Retrieval is performed by image example where a query image is given as input by the user and an appropriate similarity measure is used to find the best matches in the corresponding feature space. This approach suffers from the fact that there is a large discrepancy between the low-level visual features that one can extract from an image and the semantic interpretation of the image’s content that a particular user may have in a given situation. That is, users seek semantic similarity, but we can only provide similarity based on low-level visual features extracted from the raw pixel data, a situation known as the semantic gap. The selection of an appropriate similarity measure is thus an important problem. Since visual content can be represented by different attributes, the combination and importance of each set of features varies according to the user’s semantic intent. Thus, the retrieval strategy should be adaptive so that it can accommodate the preferences of different users. Relevance feedback (RF) learning has been proposed as a technique aimed at reducing the semantic gap. It works by gathering semantic information from user interaction. Based on the user’s feedback on the retrieval results, the retrieval scheme is adjusted. By providing an image similarity measure under human perception, RF learning can be seen as a form of supervised learning that finds relations between high-level semantic interpretations and low-level visual properties. That is, the feedback obtained within a single query session is used to personalize the retrieval strategy and thus enhance retrieval performance. In this chapter we present an overview of CBIR and related work on RF learning. We also present our own previous work on a RF learning-based probabilistic region relevance learning algorithm for automatically estimating the importance of each region in an image based on the user’s semantic intent.

Article Free Access Share on Fast multiresolution image querying Authors: Charles E. Jacobs Department of Computer Science and Engineering, University of Washington, Seattle, Washington Department of Computer Science and Engineering, University of Washington, Seattle, WashingtonView Profile , Adam Finkelstein Department of Computer Science and Engineering, University of Washington, Seattle, Washington Department of Computer Science and Engineering, University of Washington, Seattle, WashingtonView Profile , David H. Salesin Department of Computer Science and Engineering, University of Washington, Seattle, Washington Department of Computer Science and Engineering, University of Washington, Seattle, WashingtonView Profile Authors Info & Claims SIGGRAPH '95: Proceedings of the 22nd annual conference on Computer graphics and interactive techniquesSeptember 1995 Pages 277–286https://doi.org/10.1145/218380.218454Online:15 September 1995Publication History 391citation1,811DownloadsMetricsTotal Citations391Total Downloads1,811Last 12 Months24Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Image segmentation is an old and difficult problem. One of the fundamental weaknesses of current computer vision systems to be used in practical applications is their inability to adapt the segmentation process as real-world changes occur in the image. We present the first closed loop image segmentation system which incorporates a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions such as time of day, time of year, clouds, etc. The segmentation problem is formulated as an optimization problem and the genetic algorithm efficiently searches the hyperspace of segmentation parameter combinations to determine the parameter set which maximizes the segmentation quality criteria. The goals of our adaptive image segmentation system are to provide continuous adaptation to normal environmental variations, to exhibit learning capabilities, and to provide robust performance when interacting with a dynamic environment. We present experimental results which demonstrate learning and the ability to adapt the segmentation performance in outdoor color imagery.

A closed-loop image segmentation system that incorporates a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions is presented. The genetic algorithm efficiently searches the hyperspace of segmentation parameter combinations to determine the parameter set which maximizes the segmentation quality criteria. A summary of the experimental results that demonstrates the ability to perform adaptive image segmentation and to learn from experience using a collection of outdoor color imagery is given. >

This paper presents an image retrieval system based on 2D shape information. Query shape objects and database images are represented by polygonal approximations of their contours. Afterwards they are encoded, using geometric features, in terms of predefined structures. Shapes are then located in database images by a voting procedure on the spatial domain. Then an alignment matching provides a probability value to rank de database image in the retrieval result. The method allows to detect a query object in database images even when they contain complex scenes. Also the shape matching tolerates partial occlusions and affine transformations as translation, rotation or scaling.

Image searching is most interesting in the field of the computer vision. Every day many digital images are coming into the web. User is attracted to automatic image retrieval from this large dataset. Many methods which are introduced in this last ten years for image retrieval based on the similarity, size of database, image classification, similar group of images finding, performance of retrieval. when the size of database is increasing image similarity finding It is big task for the researchers to give the efficient solutions. Content-Based Image Retrieval (CBIR) systems are used in order to retrieve image from image dataset. In our proposed method, we are utilizing clustering method for retrieve the images. Each Image converted into gray form and calculated the threshold values for 10000 images for each image extracted cluster mean values using k-mean method and stored into the database. In first method query image mean values are comparing using mean distances between query image and database images. In second method group of images are selecting from the image database with similar features and finding the distance difference of the each image and considered the squires of the all distances and make a sum and divide with the number of cluster values then make in sequential order of the images. Main aim of this work is to extract images with good similarity when the images that are retrieved based on query image. Above mention methods are given good performance.

Visual localization is an attractive problem that estimates the camera localization from database images based on the query image. It is a crucial task for various applications, such as autonomous vehicles, assistive navigation and augmented reality. The challenging issues of the task lie in various appearance variations between query and database images, including illumination variations, dynamic object variations and viewpoint variations. In order to tackle those challenges, Panoramic Annular Localizer into which panoramic annular lens and robust deep image descriptors are incorporated is proposed in this paper. The panoramic annular images captured by the single camera are processed and fed into the NetVLAD network to form the active deep descriptor, and sequential matching is utilized to generate the localization result. The experiments carried on the public datasets and in the field illustrate the validation of the proposed system.

This paper introduces a new CBIR system based on two different approaches in order to achieve the retrieval efficiency and accuracy. Color and texture information is extracted and used in this work to form the feature vector. To do the texture feature extraction this system uses DCT and DCT Wavelet transform to generate the feature vectors of the query and database images. Color information extraction process includes separation of image into R, G and B planes. Further each plane is divided into 4 blocks and for each block row mean vectors are calculated. DCT and DCT wavelet is applied over row mean vector of each block separately and 4 sets of DCT and DCT wavelet coefficients are obtained respectively. Out of these few coefficients are selected from each block and arranged in consecutive order to form the feature vector of the image. Variable size feature vectors are formed by changing the no of coefficients selected from each row vector. Total 18 different sets are obtained by changing the no of coefficients selected from each block. These two different feature databases obtained using DCT and DCT wavelet are then tested using 100 query images from 10 different categories. Euclidean distance is used as similarity measure to compare the image features. Euclidean distance calculated is sorted into ascending order and cluster of first 100 images is selected to count the images which are relevant to the query image. Results are further refined using second level thresholding which uses three criteria which can be applied to first level results. Results obtained are showing the better performance by DCT wavelet as compare to DCT transform. Keywords-component; DCT; DCT wavelet; Eucidean distance. I. INTRODUCTION Large amount of images are being generated, stored and used daily in various real life applications through various fields like engineering, medical sciences, biometrics, architectural designs and drawings and many other areas. Although various techniques are being designed and used to store the images efficiently, still it demands to search new effective and accurate techniques to retrieve these images easily from large volume of databases. Text based image retrieval techniques have tried in this direction which has got many constraints and drawbacks associated with it which is continuously encouraging the researchers to come up with the new techniques to retrieve the images based on contents instead of text annotations. Image contents are broadly classified into global and local contents. Local contents define the local attributes of the image like color, shape and texture

This paper develops a general framework of image retrieval, named A3, by introducing an auxiliary set of samples (object references), each of which is annotated with semantic attributes (tags). Given a query image (without tags), we first map it into the references by a non-convex sparse coding formulation, which jointly optimizes appearance reconstruction of the query and semantics consistency among references. A novel algorithm, namely semantic coherent coding (SCC), is presented to solve this optimization. As a result, a subset of references is selected to augment the query for retrieval. Then we can rank images to be retrieved by intuitively matching them together with the query and the constructed subset. The advantages of our approach are validated on the public datasets (Caltech-256 and ImageNet), and summarized as follows: (1) The auxiliary set of references gives rise to an alternating way for query augmentation while utilizing semantics. (2) The semantics of references can also be propagated into the query and database images.