Techniques Used For Extracting Useful Information From Images

The digital image of the face of a person can be matched against a record of other images by using face recognition software. In case that some of the images in the database go with the digitized image, the owner is reported about it by the system. Automatic face recognition has been studied and investigated extensively since 1990s and its usage is turning out to be no-nonsense in the present times. Although a number of automatic face recognition applications are relatively agreeable and harmless (access regulation to armaments, currency, illegal proof, nuclear equipment/supplies etc), there are still several dangers of face recognition systems that include violation of privacy and civil liberties, unrestrained exploitation, identity theft, illegal use of database, free access to personal data on social media etc. (Agre, 2001).

Thus our proposed optimal feature subset selection based on multi-level feature subset selection produced better results based on number of subset feature produced and classifier performance. The future scope of the work is to use these features to annotate the image regions, so that the image retrieval system can retrieve relevant images based on image semantics.

Out of all of my interests in computer technology, it is also the topic of my graduation thesis. Facial recognition has recently accomplished great achievements since its acknowledgment rate at many institutions has nearly reached 100%, but there are still many obstacles. One of them is the difficulty in recognizing children, understanding the meaning behind the picture and issues with age variation. The performance of most facial recognition algorithms significantly vary with different sample sets. My research is preliminarily and targeted to the aforementioned

Images with higher similarity than the threshold are returned as an output. System which uses the content of the object for Information Retrieval is the CBIR.

Face detection is a growing and an important tool in security these days. It must be applied before face recognition. There are many methods for recognizing faces and a few of them are discussed in the paper.

CSU Face Identification Evaluation System is used to evaluate the performance of the technique and it is found that bottom up approach proposed has better identification rate tested on 104 images.

Data mining is a popular technology for extracting interesting information for multimedia data sets, such as audio, video, images, graphics, speech, text and combination of several types of data set. Multimedia data are unstructured data or semi-structured data. These data are stored in multimedia database, multimedia mining which is used to find information from large multimedia database system, using multimedia techniques and powerful tools. This paper analyzes about the use of essential characteristics of multimedia data mining, retrieving information is one of the goals of data mining and different issues have been discussed. The current approaches and techniques are explained for mining multimedia data. In this paper applications and models of multimedia mining are discussed clearly. Nowadays Multimedia mining has become popular field in research area.

Woodward, J. D., Horn, C., Gatune, J., & Thomas, A. (2003). Biometrics A Look at Facial Recognition. Retri

There are so many techniques used for the face recognition in all over the world. It plays a very important role in our daily lives nowadays as it protect us from thieves, used for our identity etc. Some of the techniques for face recognition includes eigenfaces, principle component analysis, independent component analysis, elastic bunch graph machine, range imaging, gabor wavelet networks and many more.

Recently, appearance-based face recognition has received tons of attention. In general, a face image of size n1 × n2 is delineating as a vector within the image house Rn1 × n2. We have a tendency to denote

When manipulating massive image databases, a good indexing is necessary. Processing every single item in a database, when performing queries, is extremely inefficient and slow. When working with images, the feature vectors are used as the basis of the index. Popular multi-dimensional indexing methods include the R-tree and the R*-tree algorithms (Long et al., 2003). The Self Organizing Map (SOM) is also one of the indexing structures (Laaksonen et al., 2000). Usage of indexing techniques during searching reduces processing time and thus retrieves images quickly.

Face Recognition is a technology to extract facial features by computer and a technique for authentication according to the characteristics of these features. Face Recognition

Abstract— As there is vivid implementations in the multimedia technologies, users find it complex for retrieving information with traditional image retrieval techniques. The CBIR techniques are becoming an efficient techniques for exact and fast retrieval of images. CBIR is the technique which uses visual features of image such as shape, color, texture etc, to search the image based on the user requirements from large database according to the user request in the form of a query. In this paper various techniques of CBIR such as k-means clustering, k-nearest neighbors Algorithm (KNN), color structure descriptor (CSD), Text based image retrieval (TBIR) techniques which increase the effectiveness of fast retrieval are discussed and analyzed.

content in the images and deriving conclusions. MATLAB is one of the many such software available

CBIR is a technique which uses visual content to search and compares images from large scale image databases according to the interest of the users. In this process firstly, the