MetaWeather: Few-Shot Weather-Degraded Image Restoration via Degradation Pattern Matching

Real-world vision tasks frequently suffer from the appearance of adverse weather conditions including rain, fog, snow, and raindrops in captured images. Recently, several generic methods for restoring weather-degraded images have been proposed, aiming to remove multiple types of adverse weather effects present in the images. However, these methods have considered weather as discrete and mutually exclusive variables, leading to failure in generalizing to unforeseen weather conditions beyond the scope of the training data, such as the co-occurrence of rain, fog, and raindrops. To this end, weather-degraded image restoration models should have flexible adaptability to the current unknown weather condition to ensure reliable and optimal performance. The adaptation method should also be able to cope with data scarcity for real-world adaptation. This paper proposes MetaWeather, a few-shot weather-degraded image restoration method for arbitrary weather conditions. For this, we devise the core piece of MetaWeather, coined Degradation Pattern Matching Module (DPMM), which leverages representations from a few-shot support set by matching features between input and sample images under new weather conditions. In addition, we build meta-knowledge with episodic meta-learning on top of our MetaWeather architecture to provide flexible adaptability. In the meta-testing phase, we adopt a parameter-efficient fine-tuning method to preserve the prebuilt knowledge and avoid the overfitting problem. Experiments on the BID Task II.A dataset show our method achieves the best performance on PSNR and SSIM compared to state-of-the-art image restoration methods. Code is available at (TBA).Comment: 12 pages, 6 figure

Pengelompokan Gambar Berdasarkan Fitur Warna Dan Tekstur Menggunakan FGKA Clustering (Fast Genetics K-Means Algorithm) Untuk Pencocokan Gambar

A large collections of digital images are being created. Usually, the only way of searching these collections was by using meta data (like caption or keywords). This way is not effective, impractical, need a big size of database and giving inaccurate result. Recently, it has been developed many ways in image retrieval that use image content (color, shape, and texture) that more recognised with CBIR ( Content Based Images Retrieval). The use of centroid produced from clustered HSV Histogram and Gabor Filter using FGKA, can be used for searching parameter. FGKA is merger of Genetic Algorithm and Kmeans Clustering Algorithm. FGKA is always converge to global optimum. Image Clustering and Matching based on color-texture feature are better than based on color feature only, texture only or using non-clustering method. Keywords: Genetics Algorithm, K-Means Clustering, CBIR, HSV Histogram, Gabor Filter

Trademark image retrieval by local features

The challenge of abstract trademark image retrieval as a test of machine vision algorithms has attracted considerable research interest in the past decade. Current operational trademark retrieval systems involve manual annotation of the images (the current ‘gold standard’). Accordingly, current systems require a substantial amount of time and labour to access, and are therefore expensive to operate. This thesis focuses on the development of algorithms that mimic aspects of human visual perception in order to retrieve similar abstract trademark images automatically. A significant category of trademark images are typically highly stylised, comprising a collection of distinctive graphical elements that often include geometric shapes. Therefore, in order to compare the similarity of such images the principal aim of this research has been to develop a method for solving the partial matching and shape perception problem. There are few useful techniques for partial shape matching in the context of trademark retrieval, because those existing techniques tend not to support multicomponent retrieval. When this work was initiated most trademark image retrieval systems represented images by means of global features, which are not suited to solving the partial matching problem. Instead, the author has investigated the use of local image features as a means to finding similarities between trademark images that only partially match in terms of their subcomponents. During the course of this work, it has been established that the Harris and Chabat detectors could potentially perform sufficiently well to serve as the basis for local feature extraction in trademark image retrieval. Early findings in this investigation indicated that the well established SIFT (Scale Invariant Feature Transform) local features, based on the Harris detector, could potentially serve as an adequate underlying local representation for matching trademark images. There are few researchers who have used mechanisms based on human perception for trademark image retrieval, implying that the shape representations utilised in the past to solve this problem do not necessarily reflect the shapes contained in these image, as characterised by human perception. In response, a ii practical approach to trademark image retrieval by perceptual grouping has been developed based on defining meta-features that are calculated from the spatial configurations of SIFT local image features. This new technique measures certain visual properties of the appearance of images containing multiple graphical elements and supports perceptual grouping by exploiting the non-accidental properties of their configuration. Our validation experiments indicated that we were indeed able to capture and quantify the differences in the global arrangement of sub-components evident when comparing stylised images in terms of their visual appearance properties. Such visual appearance properties, measured using 17 of the proposed metafeatures, include relative sub-component proximity, similarity, rotation and symmetry. Similar work on meta-features, based on the above Gestalt proximity, similarity, and simplicity groupings of local features, had not been reported in the current computer vision literature at the time of undertaking this work. We decided to adopted relevance feedback to allow the visual appearance properties of relevant and non-relevant images returned in response to a query to be determined by example. Since limited training data is available when constructing a relevance classifier by means of user supplied relevance feedback, the intrinsically non-parametric machine learning algorithm ID3 (Iterative Dichotomiser 3) was selected to construct decision trees by means of dynamic rule induction. We believe that the above approach to capturing high-level visual concepts, encoded by means of meta-features specified by example through relevance feedback and decision tree classification, to support flexible trademark image retrieval and to be wholly novel. The retrieval performance the above system was compared with two other state-of-the-art image trademark retrieval systems: Artisan developed by Eakins (Eakins et al., 1998) and a system developed by Jiang (Jiang et al., 2006). Using relevance feedback, our system achieves higher average normalised precision than either of the systems developed by Eakins’ or Jiang. However, while our trademark image query and database set is based on an image dataset used by Eakins, we employed different numbers of images. It was not possible to access to the same query set and image database used in the evaluation of Jiang’s trademark iii image retrieval system evaluation. Despite these differences in evaluation methodology, our approach would appear to have the potential to improve retrieval effectiveness