Joydeep Mukherjee

Content based Image Retrieval using Histogram, Color and Edge

Content Based Image Retrieval (CBIR) is a process to retrieve a stored image from database by supplying an image as query instead of text. This can be done by proper feature extraction and querying process. The features like histogram, color values and edge detection plays very vital role in proper image retrieval. Here we have implemented a method of image retrieval using the histogram, color and edge detection features. In this method we used image segmentation in order to get a better accuracy percentage and this proved itself a very successful approach. We used our own computation method as well as some Matlab functions. Canny‟s edge detection technique and color values extraction after image segmentation gives a better accuracy level to our system. Finally we get top images matching to our query image using Euclidean Distance method. General Terms Image recognition by content.

Fish Shape Recognition using Multiple Shape Descriptors

This paper studies recognition of fish shapes using both Region based and Contour based shape based descriptors[9]. Moment Invariants are chosen as the Region based descriptor and the Simple (geometric) shape descriptors (SSD) are used as Contour based shape descriptors. The shapes are varied through scaling and rotation. Manhattan Distance is used as the classifier. The study of the recognition rate by using moment invariants and simple shape descriptors is done separately. Each moment invariant (M1 , M2 , M3 , M4 and M5) is studied separately and jointly. Then simple shape descriptors are combined with moment invariants to get hybrid feature vectors for improving recognition rate. General Terms Fish Shape recognition

An Efficient Technique to Locate Number Plate using Morphological Edge Detection and Character Matching Algorithm

This paper describes an efficient technique of locating and extracting license plate and recognizing each segmented character. The proposed model can be subdivided into four partsDigitization of image, Edge Detection, Separation of characters and Template Matching. In this work, we propose a method which is based on morphological operations where different Structuring Elements (SE) are used to maximally eliminate non-plate region and enhance plate region. Character segmentation is done using Connected Component Analysis. Correlation based template matching technique is used for recognition of characters. This system is implemented using MATLAB7. 4. 0. The proposed system is mainly applicable to Indian License Plates.

Content based Natural Image Retrieval using Histogram, Segmentation and Edge

Content Based Image Retrieval (CBIR) is a process to retrieve a stored image from database by supplying an image as query instead of text. This can be done by proper feature extraction and querying process. The features like histogram, color values and edge detection plays very vital role in proper image retrieval. Here we have implemented a method of image retrieval using the histogram, color and edge detection features. In this method we used image segmentation in order to get a better accuracy percentage and this proved itself to be a very successful approach. We used our own computation method as well as some MATLAB functions. Canny’s edge detection technique and color values extraction after image segmentation gives a better accuracy level to our system. Finally we get top images matching to our query image using Euclidean distance.