Image retrieval and related operations are always a 'hotspot' in the information era. Content-based image retrieval (CBIR) is a vastly developing area in the multimedia technology domain. To enhance security, we apply watermarking technique into the retrieval system and propose an approach for JEPG image retrieval. The proposed image retrieval system consists of three main phases, offline process, online retrieval process and the feedback process. The offline process aims at the feature vector extraction from the image. Later these features will be stored in the database. When it comes to the online retrieval process, it actually extracts the image features from the input image and matches these feature vectors with those available in the image database. In order to overcome the possible dissimilarity between bottom features and high-level semantics in the image retrieval; we introduce the feedback network to strengthen the retrieval efficiency. This is a simple categorized screening. Such a feedback scenario makes the system more user-friendly and effective. The proposed feedback screening strategy filters the images from irrelevant categories and enriches the final result with more relevant images

Due to the revolutionary explosion of internet and digital technologies, the requisite to have a system that organizes the copiously available digital images for easy categorization and retrieval has been imposed. Nowadays, Content Based Image Retrieval (CBIR) has become a solution and source of accurate and fast retrieval. CBIR uses the visual contents to retrieve relevant images from large databases according to user's interests. The visual contents (color, texture, shape etc) serve as the features for the images. Features are measurements of

Video scene decomposition with the motion picture parser. 21 to manage lm-cutting of one or more video clips. References 1] H. J. Zhang and S. W. Smoliar. Developing power tools for video indexing and retrieval. A magniier tool for video data. 20 SEGM. In this state the C module accepts the following messages: null(): is an implicit request to carry the search of a camera break on next frame. If one end of the video clip is reached, or a camera break is detected, the C program returns to the NORMAL state. stop(): requests to stop the automatic segmentation and to return to the NORMAL state. 6 Conclusions and future work In this paper we presented VCB, an environment for video clip browsing. At present, it consists in a set of integrated and interactive tools designed to provide an environment for easily navigating through a video stream. The available browsing modality are the Video Recorder, the Tree Exploration and the Packed Frames. A peculiarity of the VCB system is the organization of the browsers into a common environment that allows the user to work with them in a exible interleaved manner. Additionally, an Annotation mode for textual mark is available for manual video indexing, as well as a Video Clip Analysis mode that provides some preliminary algorithms for the automatic segmentation of video clips. This modality can be viewed as a facility for browsing as well as a rst step towards the automatic indexing of video material. Future extensions of the VCB and more include the introduction of new browsing tools and, more important, the development of the ...and more part of the system. The Annotation mode will be integrated with the automatic annotation of frames using information coming from the Video Clip Analysis. Furthermore, a database management system will be added in order to support information retrieval based on the text les associated to the frames. Another extension, involving the Video Clip Analysis section, will be in the direction of integrating the system with specialized modules for the automatic indexing of the video clip. We plan to endow the system with some modules implemented in the past. For example, the system could be capable of localizing and reading text in the frames ((23]), or localizing and recognizing faces ((24]). Some auxiliary modules will be developed for the automatic selection of the parameters, e.g. thresholds, which the algorithms depend on. …

This study attempts to provide fast indexing technique which will help to retrieve images from the database quickly and focuses on how to retrieve most relevant images from the database. The need for efficient Content-based Image Retrieval (CBIR) has increased tremendously in many application areas such as biomedicine, military, commerce, education and web image classification and searching. The semantic gap is the greatest challenge in the CBIR. The semantic gap is the lack of coincidence between the information that one can extract from the visual data and the interpretation that the same data have for a user in a given situation. The CBIR uses the visual contents of an image such as color, shape, texture and spatial layout to represent and index the image. In typical content-based image retrieval systems, the visual contents of the images in the database are extracted and described by multi-dimensional …

Searching in image databases using image content has made the transition from the laboratory to consumer software. Storm Software is a pioneer in bringing these techniques to shrink- wrapped software applications, and this presentation describes some of the methods we use in our products and some of the experiences we have had in bringing this new technology to consumers. We describe the scope of the problem we are trying to solve as well as some of the algorithms and interfaces we used. We also describe some of the rationales (based on theory as well as on user testing) we had for the various design decisions we made. Finally, we describe some of the challenges and opportunities we see ahead. Descriptions and screen shots of two software products implementing image searching (EasyPhoto and Apple PhotoFlash) are provided. Both products were developed by Storm Software.

In image retrieval based on color, the weighted distance between color histograms of two images, represented as a quadratic form, may be defined as a match measure. However, this distance measure is computationally expensive (naively O(N2) and at best O(N) in the number N of histogram bins) and it operates on high dimensional features (O(N)). We propose the use of low-dimensional, simple to compute distance measures between the color distributions, and show that these are lower bounds on the histogram distance measure. Results on color histogram matching in large image databases show that prefiltering with the simpler distance measures leads to significantly less time complexity because the quadratic histogram distance is now computed on a smaller set of images. The low-dimensional distance measure can also be used for indexing into the database.

In the QBIC (Query By Image Content) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, shape, position, and dominant edges of image objects and regions. Potential applications include medical (“Give me other images that contain a tumor with a texture like this one”), photo-journalism (“Give me images that have blue at the top and red at the bottom”), and many others in art, fashion, cataloging, retailing, and industry. We describe a set of novel features and similarity measures allowing query by image content, together with the QBIC system we implemented. We demonstrate the effectiveness of our system with normalized precision and recall experiments on test databases containing over 1000 images and 1000 objects populated from commercially available photo clip art images, and of images of airplane silhouettes. We also present new methods for efficient processing of QBIC queries that consist of filtering and indexing steps. We specifically address two problems: (a) non Euclidean distance measures; and (b) the high dimensionality of feature vectors. For the first problem, we introduce a new theorem that makes efficient filtering possible by bounding the non-Euclidean, full cross-term quadratic distance expression with a simple Euclidean distance. For the second, we illustrate how orthogonal transforms, such as Karhunen Loeve, can help reduce the dimensionality of the search space. Our methods are general and allow some “false hits” but no false dismissals. The resulting QBIC system offers effective retrieval using image content, and for large image databases significant speedup over straightforward indexing alternatives. The system is implemented in X/Motif and C running on an RS/6000.

The purpose of our work is to outline objects on images in an interactive environment. We use an improved method based on energy minimizing active contours or `snakes.' Kass et al., proposed a variational technique; Amini used dynamic programming; and Williams and Shah introduced a fast, greedy algorithm. We combine the advantages of the latter two methods in a two-stage algorithm. The first stage is a greedy procedure that provides fast initial convergence. It is enhanced with a cost term that extends over a large number of points to avoid oscillations. The second stage, when accuracy becomes important, uses dynamic programming. This step is accelerated by the use of alternating search neighborhoods and by dropping stable points from the iterations. We have also added several features for user interaction. First, the user can define points of high confidence. Mathematically, this results in an extra cost term and, in that way, the robustness in difficult areas (e.g., noisy edges, sharp corners) is improved. We also give the user the possibility of incremental contour tracking, thus providing feedback on the refinement process. The algorithm has been tested on numerous photographic clip art images and extensive tests on medical images are in progress.

The QBIC (query by image content) project in the IBM Almaden Research Center in San Jose, CA, is conducting a theoretical, experimental, and prototyping study of the problem of querying large still image databases efficiently based on image content. Since the problem is difficult, the aim is to discover general principles, but at the same time to identify target application(s) for which concrete pilot systems will be prototyped. A number of algorithms have been developed that allow the user to search based on color, texture, and shape. The search can be focused on either image objects (areas previously outlined by the user) or on the whole images. The search argument can be all or part of a particular image, or user-selected patterns of color, texture, or shape selected from 'pickers', or any weighted combination of these patterns. An example of a search by shape and color, and associated result is given. The results of initial experimentation are encouraging.<>

In the query by image content (QBIC) project we are studying methods to query large on-line image databases using the images' content as the basis of the queries. Examples of the content we use include color, texture, and shape of image objects and regions. Potential applications include medical (`Give me other images that contain a tumor with a texture like this one'), photo-journalism (`Give me images that have blue at the top and red at the bottom'), and many others in art, fashion, cataloging, retailing, and industry. Key issues include derivation and computation of attributes of images and objects that provide useful query functionality, retrieval methods based on similarity as opposed to exact match, query by image example or user drawn image, the user interfaces, query refinement and navigation, high dimensional database indexing, and automatic and semi-automatic database population. We currently have a prototype system written in X/Motif and C running on an RS/6000 that allows a variety of queries, and a test database of over 1000 images and 1000 objects populated from commercially available photo clip art images. In this paper we present the main algorithms for color texture, shape and sketch query that we use, show example query results, and discuss future directions.