CONTENT BASED IMAGE RETRIEVAL (CBIR) SYSTEM

Advancement in hardware and telecommunication technology has boosted up creation and distribution of digital visual content. However this rapid growth of visual content creations has not been matched by the simultaneous emergence of technologies to support efficient image analysis and retrieval. Although there are attempt to solve this problem by using meta-data text annotation but this approach are not practical when it come to the large number of data collection. This system used 7 different feature vectors that are focusing on 3 main low level feature groups (color, shape and texture). This system will use the image that the user feed and search the similar images in the database that had similar feature by considering the threshold value. One of the most important aspects in CBIR is to determine the correct threshold value. Setting the correct threshold value is important in CBIR because setting it too low will result in less image being retrieve that might exclude relevant data. Setting to high threshold value might result in irrelevant data to be retrieved and increase the search time for image retrieval. Result show that this project able to increase the image accuracy to average 70% by combining 7 different feature vector at correct threshold value. ii

Attention Based Auto Image Cropping

Many images contain salient regions that are surrounded by too much uninteresting background material and are not as enlightening as a sensibly cropped version. The choice of the best picture window both at capture time and during subsequent processing is normally subjective and a wholly manual task. This paper proposes a method of automatically cropping visual material based upon a new measure of visual attention that reflects the informativeness of the image

CONTENT BASED IMAGE RETRIEVAL (CBIR) SYSTEM

Advancement in hardware and telecommunication technology has boosted up creation and distribution of digital visual content. However this rapid growth of visual content creations has not been matched by the simultaneous emergence of technologies to support efficient image analysis and retrieval. Although there are attempt to solve this problem by using meta-data text annotation but this approach are not practical when it come to the large number of data collection. This system used 7 different feature vectors that are focusing on 3 main low level feature groups (color, shape and texture). This system will use the image that the user feed and search the similar images in the database that had similar feature by considering the threshold value. One of the most important aspects in CBIR is to determine the correct threshold value. Setting the correct threshold value is important in CBIR because setting it too low will result in less image being retrieve that might exclude relevant data. Setting to high threshold value might result in irrelevant data to be retrieved and increase the search time for image retrieval. Result show that this project able to increase the image accuracy to average 70% by combining 7 different feature vector at correct threshold value. ii

Algorithms for Automatic Image Cropping

Hlavním cílem této bakalářské práce je studium a implementace metod, které umožňují automatický ořez fotografií tak, aby výsledek ořezu byl použitelný z fotografického hlediska. V této práci jsou provedeny experimenty s třemi vybranými metodami a na jejich základě jsou diskutovány možné optimalizace. Jsou zde také popsány konkrétní vlastnosti jednotlivých algoritmů a provedeno zhodnocení výsledků automatického ořezu podle uživatelského testování.The main goal of this bachelor thesis is study and implementation of methods that can automatically crop images, so the result has good usability from a photographic view. In this thesis are made experiments with three methods and possible optimalizations are discussed. The properties of cropping algorithms are also described here and the evaluation of implemented algoritmhs is made according to user testing.