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Image Database Query by Example
Dobrotka, Matúš ; Hradiš, Michal (referee) ; Veľas, Martin (advisor)
This thesis deals with content-based image retrieval. The objective of the thesis is to develop an application, which will compare different approaches of image retrieval. First basic approach consists of keypoints detection, local features extraction and creating a visual vocabulary by clustering algorithm - k-means. Using this visual vocabulary is computed histogram of occurrence count of visual words - Bag of Words (BoW), which globally represents an image. After applying an appropriate metrics, it follows finding similar images. Second approach uses deep convolutional neural networks (DCNN) to extract feature vectors. These vectors are used to create a visual vocabulary, which is used to calculate BoW. Next procedure is then similar to the first approach. Third approach uses extracted vectors from DCNN as BoW vectors. It is followed by applying an appropriate metrics and finding similar images. The conclusion describes mentioned approaches, experiments and the final evaluation.
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Photo Instead of QR Code
Štol, Jakub ; Španěl, Michal (referee) ; Beran, Vítězslav (advisor)
This thesis focuses on create system, which according to user's photo search the most similar photo in dataset a display additional information about this photo such as photo content, place and author. The system is primarily focused on fields, where the user wants to use this system and get information, which he want such as museums or galleries. Theoretical part focuses on concept system according to methods of image processing and searching image in the dataset. Practical part focuses on concept creating web application, which display more information about the photo via web interface.
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Image Retrieval Based on Color Histograms
Sailer, Zbyněk ; Hradiš, Michal (referee) ; Beran, Vítězslav (advisor)
This thesis deals with description of existing methods of image retrieval. It contains set of methods for image description, coding of global and local descriptor (SIFT, etc.) and describes method of effective searching in multidimensional space (LSH). It continues with proposal and testing of three global descriptors using color histograms, histogram of gradients and the combination of both. The last part deals with similar image retrieval using proposed descriptors and the indexing method LSH and compares the results with the existing method. Product of this work is an experimental application which demonstrates the proposed solution.
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Android Application with Content Based Image Retrieval
Ligač, Filip ; Angelov, Michael (referee) ; Bartoš, Peter (advisor)
This thesis deals with the development on the mobile platform Android and algorithms for comparing images. The work identifies different ways of getting information and representation of these images and comparing them together. The theoretical part describes all necessary tools which are needed for development and a little description of their functionality. The practical part is devoted to describing the implementation of the application which detects hockey cards according to the photo taken by user. This part also contains testing results with evaluating success of each method for detection and their usefulness on mobile platform. In the conclusion there is a review of overall results and possible upgrades of the application.
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Digital Image Processing of Cross-section Samples
Beneš, Miroslav ; Zitová, Barbara (advisor) ; Matula, Pavel (referee) ; Pelagotti, Anna (referee)
The thesis is aimed on the digital analysis and processing of micro- scopic image data with a focus on cross-section samples from the artworks which fall into cultural heritage domain. It contributes to solution of two different problems of image processing - image seg- mentation and image retrieval. The performance evaluation of differ- ent image segmentation methods on a data set of cross-section images is carried out in order to study the behavior of individual approaches and to propose guidelines how to choose suitable method for segmen- tation of microscopic images. Moreover, the benefit of segmenta- tion combination approach is studied and several distinct combination schemes are proposed. The evaluation is backed up by a large number of experiments where image segmentation algorithms are assessed by several segmentation quality measures. Applicability of achieved re- sults is shown on image data of different origin. In the second part, content-based image retrieval of cross-section samples is addressed and functional solution is presented. Its implementation is included in Nephele system, an expert system for processing and archiving the material research reports with image processing features, designed and implemented for the cultural heritage application area. 1
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Matching Images to Texts
Hajič, Jan ; Pecina, Pavel (advisor) ; Průša, Daniel (referee)
We build a joint multimodal model of text and images for automatically assigning illustrative images to journalistic articles. We approach the task as an unsupervised representation learning problem of finding a common representation that abstracts from individual modalities, inspired by multimodal Deep Boltzmann Machine of Srivastava and Salakhutdinov. We use state-of-the-art image content classification features obtained from the Convolutional Neural Network of Krizhevsky et al. as input "images" and entire documents instead of keywords as input texts. A deep learning and experiment management library Safire has been developed. We have not been able to create a successful retrieval system because of difficulties with training neural networks on the very sparse word observation. However, we have gained substantial understanding of the nature of these difficulties and thus are confident that we will be able to improve in future work.
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Automatic suggestion of illustrative images
Odcházel, Ondřej ; Pecina, Pavel (advisor) ; Holub, Martin (referee)
The objective of this thesis is to implement a web application designed for recommendation of stock photos. The application gets the input from newspaper articles in Czech or English and, based on the text itself, suggests appropriate stock photos. The implemented application also searches images according to visual similarity. The thesis deals with theoretical aspects of keywords extraction and language of text detection. Further it analyzes possibilities of efficient search for similar vectors that are used in the search component for visually similar images. It also describes the possibilities in development of modern web frontend and backend. The quality of algorithm for recommending stock photos is tested on users. Powered by TCPDF (www.tcpdf.org)
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Photo Instead of QR Code
Štol, Jakub ; Španěl, Michal (referee) ; Beran, Vítězslav (advisor)
This thesis focuses on create system, which according to user's photo search the most similar photo in dataset a display additional information about this photo such as photo content, place and author. The system is primarily focused on fields, where the user wants to use this system and get information, which he want such as museums or galleries. Theoretical part focuses on concept system according to methods of image processing and searching image in the dataset. Practical part focuses on concept creating web application, which display more information about the photo via web interface.
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Application for automatic recognition of textures in map data
Šípoš, Peter ; Skopal, Tomáš (advisor) ; Lokoč, Jakub (referee)
This work has aimed to implement an easy-to-use application which can be used to navigate through aerial imagery, assign sections of this image for different classes. Based on these category assignments the application can autonomously assign categories to so-far unknown fields, hence it helps the user in further classification. The output of the application is an index file, which can serve as underlying dataset for further analysis of a given area from geographic or economic point-of-view. To fulfil this task the program uses standard MPEG-7 descriptors to perform the feature extraction upon which the classification relies.
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Known-Item Search in Image Datasets Using Automatically Detected Keywords
Souček, Tomáš ; Lokoč, Jakub (advisor) ; Peška, Ladislav (referee)
Known-item search represents a scenario, where a user searches for one particular image in a given collection but does not know where it is located. The thesis focuses on the design and evaluation of a keyword retrieval model for known-item search in image collections. We use a deep neural network trained on a custom dataset to annotate the images. We design complex yet easy-to-use query interface for fast image retrieval. We use/design several types of artificial users to estimate the model's performance in an interactive setting. We also discuss our successful participation at two international competitions. 1
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