Graphical Search for Images by PictureFinder (original) (raw)
Related papers
Proceedings of the 2nd international symposium on Smart graphics - SMARTGRAPH '02, 2002
The explosion of storage media size and bandwidth has led to huge image databases. Methods are needed to find a particular image based on a crude description by the user. Keywording is not only tedious, but also subjective and therefore often incorrect. Available visual query systems have different properties, and are mostly based on some image transformation. An alternative visual query system is introduced, which finds an image similar to a user drawn sketch, or to any other reference image. A descriptor is created for each image in the database, and for the query image. Descriptors are compared in order to find the best matches. Descriptors are computed by inserting a limited number of quasi-random rectangles in the image, and computing the average colors of the rectangles. Furthermore, a reduced color histogram is computed and stored in the descriptor. The difference between descriptors is calculated as the weighted average of CIE LUV differences between corresponding rectangles. Using the Contrast Sensitivity Function this average is adapted to the users perception. The metric used for comparing images operates in the original image space, which makes the whole algorithm intuitive and easy to understand, and enables the comparison of images sections, as well.
Using Regions of Interest for Adaptive Image Retrieval
2008
Content-based image retrieval mainly follows a Query-by-Example approach and therefore requires well selected examples to start an initial search. This position paper describes how Regions of Interest (ROI) can be used to better adapt the system to the user's information needs. In particular, it highlights how novel input devices such as Interactive Paper or TabletPCs can be used to capture much more details about what the user is precisely looking for already at the time the query is defined or when relevance feedback is specified.
VisualSEEk: a fully automated content-based image query system
ACM multimedia, 1996
1 We describe a highly functional prototype system for searching by visual features in an image database. The VisualSEEk system is novel in that the user forms the queries by diagramming spatial arrangements of color regions. The system nds the images that contain the most similar arrangements of similar regions. Prior to the queries, the system automatically extracts and indexes salient color regions from the images. By utilizing e cient indexing techniques for color information, region sizes and absolute and relative spatial locations, a wide variety of complex joint color spatial queries may be computed.
Image Retrieval Based on Regions of Interest
IEEE Transactions on Knowledge and Data Engineering, 2003
Query-by-example is the most popular query model in recent contentbased image retrieval (CBIR) systems. A typical query image includes relevant objects (e.g., Eiffel Tower), but also irrelevant image areas (including background). The irrelevant areas limit the effectiveness of existing CBIR systems. To overcome this limitation, the system must be able to determine similarity based on relevant regions alone. We call this class of queries region-of-interest (ROI) queries and propose a technique for processing them in a sampling-based matching framework. A new similarity model is presented and an indexing technique for this new environment is proposed. Our experimental results confirm that traditional approaches, such as Local Color Histogram and Correlogram, suffer from the involvement of irrelevant regions. Our method can handle ROI queries and provide significantly better performance. We also assessed the performance of the proposed indexing technique. The results clearly show that our retrieval procedure is effective for large image data sets.
Querying by color regions using the VisualSEEk content-based visual query system
1997
VisualSEEk is a highly functional image database manipulation system that provides advanced user tools for searching, browsing and retrieving images. VisualSEEk is distinct from other content-based image query systems in that the user may query for images using both the visual properties of regions and their spatial layout. Furthermore, the image analysis for region extraction is fully automated. VisualSEEk uses a novel system for region extraction and representation based upon color sets. Through a process of color set back-projection, the system automatically extracts salient color regions from images. This paper describes the implementation of the color query system and examines its role for content-based visual query in image and video databases. The next phases of the VisualSEEk implementation will provide additional tools for querying by texture, shape, embedded text and motion features.
International Journal of Computer Applications
For the past many years we are using search engine for image retrieval. These search engines use shapes, contents, text, and caption based approach for getting relevant image from the web repository. This image repository contains billions of 2D and 3D images as well as relevant information about those images. For shape based approach user has to give dimensions of that particular image for getting relevant response. This paper describes the necessity of an efficient search engine for retrieving information about an image by uploading an image on the search engine or giving image as a query for retrieving information related to that particular image. It can be proved very helpful for a novice user who is searching information about an unknown or unfamiliar logo or image.
A User-Dependent Definition of the Information in Images and Its Use in Information Retrieval
Journal of Visual Communication and Image Representation, 1997
sets and applications, e.g., [4]. General-purpose image database management systems either define visual rele-Operational image databases are subsystems of systems which contain images, structured data, structured data complevance in terms of low level features and give the user tions, and algorithms, all relating to a particular real-world control over how features are used in the distance measure, domain. Traditional notions of image similarity therefore need e.g., , or provide comprehensive suites of relevance meato be extended to handle retrieval of mixed datatypes. In this sures each suitable for specific application areas, possibly paper, we use the structured datatypes to describe a user's with some self-learning capability [8]. In any case, different interests and background knowledge and then to ascribe a value users have different notions of relevance, so the option of to that user of the information in the system. The difference weighting components of the measure is provided, e.g., between the information values in two states of a system leads [9, 10].
An Image Retrieval System Based on Region Classification
Lecture Notes in Computer Science, 2004
I hereby declare that all information in this document has been obtained and presented in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all material and results that are not original to this work.