GEETHU P T HAFSA HASSAN HONEY MERRIN SAM SHIBIJA K

 INTRODUCTION  CURRENT SITUATION  PROPOSED SOLUTION  ALGORITHM USED  PURPOSE  GLOBAL STRUCTURE AND SUBSYSTEM  FUNCTIONAL REQUIREMENTS  PERFORMANCE MATRICES  FEATURE EXTRACTION  USE CASE DIAGRAM  DATA FLOW DIAGRAM

 ADVANTAGES  SBIR VS TEXT-BASED IMAGE RETRIEVAL  RESEARCH CHALLENGES  APPLICATIONS  FUTURE GROWTH  CONCLUSION

 The sketch based image retrieval is one of most popular, rising research areas of the digital image processing.  Goal of SBIR is to extract visual content like colour, text, or shape. Introduces design based on a free hand sketch  Making search more efficient hereby  Test results show that the sketch based system allows users an intuitive access to search-tools.

 In today’s corporate world huge data has to be managed, processed and stored  Text based search. Keywords! Is this efficient?  Therefore it is to develop a SBIR system, which can retrieve using sketches in frequently used databases.  Drawing area to sketch the required image.  Matched images to sketch are retrieved.

In earlier days, image retrieval from large image database can be done by following ways.  Automatic image annotation and retrieval using cross media relevance models  Concept based query expansion  Query system bridging the semantic gap for large image databases  Ontology-based query expansion widget for information retrieval  Detecting image purpose in world-wide web documents

 Relevance feedback is an interactive process that starts with normal CBIR.  The user input a query, and then the system extracts the image features and measure the distance with images in the database.  An initial retrieval list is then generated.  This process can be iterated many times until the user find the desired images.

Input Query Feature Extraction Similarity Measure Retrieval Result Find all Images? User’s Feedback Query update Final Retrieval Result

 In this system an efficient image retrieval algorithm based on CCM (Colour Co-occurrence Matrix) is proposed.  The CCM for each pixel of an image is found using the Hue Saturation Value (HSV) of the pixel and then compared with CCM of the images in the database and the images are retrieved

Query Image Extract HSV Formulate CCM Query Image Extract HSV Formulate CCM Compare and Match Retrieved Image SBIR using HSV model

 The goal of this paper is to develop a SBIR search engine, which with free hand sketch content can be retrieved.  The most important task is to bridge the gap between the free hand sketch and the picture.  Introducing this system into search engines makes corporate world and even other users bit more efficient in retrieval of data effectively.

1.GLOBAL STRUCTURE OF A SYSTEM  The system was designed for databases containing relatively simple images, but even in such cases large differences can occur among images in file size or resolution.  In addition, some images may be noisier, the extent and direction of illumination may vary(fig a) and so the feature vectors cannot be effectively compared. In order to avoid it, a multistep pre-processing mechanism precedes the generation of descriptors.

The retrieval has to be robust in contrast of illumination and difference of point of view.

Displaying Subsystem Preprocessing Subsystem Feature Vector generating subsystem Retrieval subsystem Database Management subsystem Feature vector image Preprocessed image Result Stock index

2. THE PREPROCESSING SUBSYSTEM  The system was designed for databases containing relatively simple images, but even in such cases large differences can occur among images in the size or resolution.  In addition, some images may be noisier, the extent and direction of illumination may vary, and so the feature vectors cannot be effectively compared.  In order to avoid it, a multistep pre-processing mechanism precedes the generation of descriptors.

Preprocessing subsystem Input Output Image Processed Image The system is for databases containing simple images

1.LOGIN MODULE

2. ADMIN MODULE

3. USER’S MODULE

SBIR is essentially an information retrieval problem. Two of the most popular evaluation measures are the,  PRECISION The precision measures the proportion of the total images retrieved which are relevant to the query. precision = number of relevant images retrieved Total retrieved

 RECALL The recall measure is defined as the fraction of the all relevant images. Recall = Total number of relevant images Number of relevant images retrieved  High precision means that less irrelevant images are returned or more relevant images are retrieved.  high recall indicates that few relevant images are missed

1.COLOUR  Colour is the most extensively used visual content for image retrieval.  The most common primary colours in computing are red, green and blue (e.g. colours used in a monitor). Usually colours are defined in three dimensional colour spaces.  In image retrieval systems, colour histogram is the most commonly used feature representation.  The colour histogram describes the proportion of pixels of each colour in an image with simple and computationally effective manner.

2.TEXTURE  Texture refers to the visual patterns with properties of homogeneity that do not result from the presence of a single colour or intensity.  It is that innate property of all surfaces that describes visual patterns such as; clouds, leaves, bricks, fabric, etc.  It contains information about the structural arrangement of surfaces and their relationship to the surrounding environment.  Texture properties include coarseness, contrast, directionality, regularity and roughness

3.SHAPE  Shape is an important criterion for matching objects based on their profile and physical structure.  Shape does not refer to the shape of an image but to the shape of a particular region that is being sought out.  Shape features can represent spatial information that is not represented by colour or texture.  It contains all the geometrical information of an object in the image which does not change generally change even when orientation or location of the object are changed.

 A Use case diagram is a list of steps, typically defining interactions between a role (known in UML as an "actor") and a system, to achieve a goal.  The actor can be a human or an external system.  The purpose of use case is to present overview of the functionality provided by the system in terms of actors, their goals and any dependencies between those use cases.

Admin Data base System Index data base images Load search images to buffer Select algorithm for search Search database for similar images

User System Upload/search images Sketch images Retrieve images Query update Data base

Indexing and searching Calculate result Display Image UserData base

Feature Extraction Image Database Compute similarity Measure Visualization

 Make convenient to retrieve data or images based on sketches so that even illiterates, who do not know to write text can also make use of system effectively.  Introducing this system into search engines makes corporate world and even other users bit more efficient in retrieval of data effectively.  Visual features, such as colour, texture, and shape information, of images are extracted automatically  Similarities of images are based on the distances between features

Image retrieval algorithms roughly belong to two categories: 1.Text-based approaches  The text-based approaches associate keywords with each stored image. 2.Content-based methods  Here retrieval of images is guided by providing a query image or a sketch generated by a user.

 In SBIR, each image that is stored in the database has its features extracted and compared to the features of the query image. It involves two steps: 1.FEATURE EXTRACTION The first step in the process is to extract image features to a distinguishable extent. 2.MATCHING The second step involves matching these features to yield a result that is visually similar.

WHAT IS THIS IMAGE…?

SKY RIVER COCONUT TREES

Giraffe Text based image retrieval system Image database CBIR System

The implementation of SBIR systems raises several research challenges. Some of these are:  Understanding image users’ needs and information- seeking behaviour.  Investigating new user interfaces for annotating, browsing, and searching based on image content.  New tools for marking/annotating images (and their regions)  Matching query and stored images in a way that reflects human similarity judgments  Providing compact storage for large image databases  Efficiently accessing stored images by content

1.MEDICAL APPLICATIONS Three large domains can instantly take advantage of SBIR techniques:  Teaching  Research  Diagnostics 2.BIODIVERSITY INFORMATION SYSTEMS Ideally, Biodiversity Information Systems (BIS) should help researchers to enhance or complete their knowledge and understanding about species and their habitats by combining textual, image content-based, and geographical queries.

3. DIGITAL LIBRARIES  One example is the digital museum of butterflies aimed at building a digital collection of Taiwanese butterflies.  This digital library includes a module responsible for content-based image retrieval based on colour, texture, and patterns.

1.WEB ORIENTED  To better organize and retrieve the almost unlimited information, web based search engines are highly desired.  Such solutions exists for text based information's 2. HIGH DIMENSIONAL INDEXING  Most currently existing research prototype systems only handle hundreds or at most thousands of images.

 The area of SBIR is a hybrid research area that requires knowledge of both computer vision and of database systems.  The technology is exciting but immature.  Among the objectives of this paper,two main aspects were taken into account. 1.The retrieval process has to be highly interactive. 2.The robustness of the method.  Frequently updated shared image database and the regular comparison of system performances would be of great benefit to the SBIR community.

 The field appears to be generating interesting and valid results, even though it has so far led to few commercial applications.  Agencies concerned with technology transfer or dissemination of best practice in fields which could potentially benefit from SBIR.