Meta tags:
description=from the beginning;

Headings (most frequently used words):

image, processing, retrieval, color, and, content, based, from, the, beginning, 2009, post, clustering, results, of, safe, cube, ip, blogs, near, us, segmentation, some, useful, notes, about, developing, complete, engine, by, using, texture, open, library, components, wondered, cbir, iranians, fuzzi, archives, recent, comments, pages, meta, blog, stats,

Text of the page (most frequently used words):
image (79), the (70), and (41), #processing (35), this (27), images (25), retrieval (21), some (20), these (20), are (19), color (19), feature (18), cbir (17), about (17), into (17), which (16), content (14), for (14), you (14), user (14), each (14), based (13), fuzzy (13), that (13), 2008 (12), using (12), clusters (12), comments (11), search (11), will (11), posted (10), zahra (10), should (10), have (9), from (9), find (9), used (9), database (9), then (9), them (9), classes (9), com (8), block (8), opencv (8), labels (8), logic (7), can (7), rank (7), texture (7), clustering (7), useful (6), may (6), but (6), system (6), order (6), similar (6), its (6), http (6), way (6), histogram (6), with (5), wordpress (5), december (5), all (5), not (5), one (5), algorithms (5), result (5), vectors (5), most (5), other (5), method (5), segmentation (5), like (4), comment (4), create (4), free (4), blog (4), applications (4), 2007 (4), features (4), here (4), found (4), prof (4), engine (4), main (4), query (4), developing (4), available (4), extraction (4), vector (4), methods (4), libraries (4), cluster (4), step (4), importance (4), space (4), pallet (4), colors (4), pixels (4), site (3), website (3), view (3), log (3), now (3), february (3), march (3), 2009 (3), iranian (3), make (3), iranians (3), google (3), know (3), both (3), called (3), indexing (3), final (3), improve (3), data (3), systems (3), desired (3), net (3), below (3), after (3), uses (3), show (3), values (3), comparable (3), shown (3), open (3), library (3), softintegration (3), package (3), class (3), first (3), respective (3), ranks (3), similarity (3), information (3), distance (3), different (3), homogeneity (3), results (3), bin (3), your (3), label (3), safe (3), cube (3), blue (3), started (2), required (2), write (2), sign (2), subscribed (2), subscribe (2), account (2), feed (2), resources (2), galleries (2), code (2), bhanu (2), prakash (2), january (2), april (2), posts (2), fuzzi (2), springer (2), tizhoosh (2), issue (2), wanted (2), articles (2), who (2), was (2), said (2), zadeh (2), decision (2), there (2), heard (2), blocking (2), related (2), issues (2), improving (2), learning (2), how (2), feedback (2), searching (2), structure (2), describe (2), better (2), input (2), has (2), tree (2), structures (2), engines (2), visual (2), leave (2), see (2), video (2), just (2), continue (2), note (2), added (2), steps (2), knowledge (2), extract (2), out (2), predefined (2), extracted (2), figure (2), components (2), scripting (2), www (2), developed (2), intel (2), academic (2), field (2), defined (2), since (2), use (2), partition (2), databse (2), limited (2), finding (2), high (2), multimedia (2), 2002 (2), refer (2), calculated (2), minkowsky (2), fundamentals (2), level (2), analogy (2), mentioned (2), criterion (2), unique (2), extracting (2), contrast (2), correlation (2), energy (2), notes (2), complete (2), object (2), define (2), region (2), 216 (2), light (2), yellow (2), post (2), objects (2), blogsearch (2), blogs (2), partitioning (2), beginning (2), get, design, name, email, loading, collapse, bar, manage, subscriptions, reader, report, privacy, already, wpthemes, 110, hits, stats, entries, meta, pages, asma, saragroups, 86emile, recent, november, archives, older, 1997, collection, approaches, understand, represent, process, their, segments, sets, representation, depend, selected, technique, problem, solved, abstract, familiar, despite, extreme, nationalist, conclude, conquered, top, when, belongs, university, waterloo, takes, highest, nice, managed, professor, too, hamid, homepage, web, page, introduced, known, berkley, continued, professors, ended, mamdani, lotfi, lotfali, asker, making, uncertain, observations, certainty, measurement, ambiguity, confusing, don, worry, million, amazing, descriptions, newborn, student, graduated, passed, course, atherwise, previous, derivation, currently, working, thesis, menas, detail, participate, wiser, store, derived, within, gets, whether, specification, searchs, whole, initially, containing, several, searched, derive, stores, efficiancy, only, text, annotated, end, traditional, also, contents, itselves, heaven, far, away, able, current, presenting, slide, beside, lecture, videolectures, look, wondered, reading, language, references, section, languages, listed, prototype, persian, farsi, فارسی, flowchart, soon, retrieved, give, opportunity, select, satisfy, his, her, more, than, others, processes, affect, previus, satisfiable, send, kernel, nearest, retriev, ready, accept, queries, entered, appears, actually, tells, retrieve, quety, easy, classify, classification, non, duty, row, stored, fast, access, descriptors, best, descriptor, appear, integer, real, tutorial, relevant, interface, following, major, embeddable, interpreter, cross, platform, plotting, numerical, computing, embedded, freely, inc, openvc, sourceforge, products, thirdparty, latest, obtained, binding, ith, programs, functions, readily, run, without, compilation, important, compatible, chipset, self, intro, yesterday, something, contains, matlab, source, mathematical, they, purpose, business, because, those, works, very, slow, develope, bussiness, application, ides, designs, purposes, phase, capable, improved, same, 256, element, part, been, belong, similatrity, measure, specific, add, special, assigned, every, last, thing, sort, rates, descending, chen, shyu, furht, spatial, 8th, international, conference, distributed, dms, san, francisco, bay, california, usa, 152, 159, reach, normalized, read, paper, reverse, together, calculate, point, forgotten, normalize, coefficient, correspond, multiplied, formula, long, zhang, dagan, feng, management, echnological, verlag, 2003, xin, zheng, locality, preserving, ioan, cleju, principal, curve, structue, thomas, deselaers, visually, satisfied, concerned, means, algorithm, respect, previously, parts, higher, impression, calculating, occurrence, matrix, shows, hsv, mode, dimention, 1000, middle, sized, categories, gallery, promised, complicated, precision, keywords, homogeneous, layout, assign, value, pixel, applied, step3, bins, batch, batches, own, reduced, rgb, implement, reduce, number, quantizing, batchs, call, dark, etc, time, doing, divided, precess, relation, explain, philosophy, behind, implementation, someone, asked, poor, simple, labeling, btng, weblogs, near, merry, christmas, start, put, going, feeds, home,

Text of the page (random words):
image processing from the beginning home about cbir applications cbir code image processing galleries useful resources image processing from the beginning feeds posts comments 2009 post december 23 2009 by zahra at the start of this blog i wanted to put useful information about image processing i am going to continue so merry christmas posted in image processing 2 comments clustering results of safe color cube may 29 2008 by zahra safe color cube partitioning into 8 clusters partitioning into 62 clusters posted in cbir color clustering content based image retrieval image processing 1 comment ip blogs near us april 28 2008 by zahra other image processing weblogs http blogsearch google com blogsearch hl en q image processing btng search blogs posted in image processing leave a comment image segmentation march 14 2008 by zahra this post is about image segmentation as someone asked my knowledge is poor in field of segmentation but i have used a simple way of extracting feature which is similar to a segmentation method it uses image labeling to find the objects in this method color homogeneity of a region will be the criterion to define the objects first i explain the philosophy behind this method then show you the implementation and its results you have to reduce the number of colors in you color pallet result of quantizing color space into some limited color batchs which we call them color labels like blue dark blue light blue yellow light yellow etc at this time you may have n color labels after that you have to label each pixels of image with these labels by doing this these pixels will be divided into n classes then you can precess the relation of these pixels and find out the object of the images these are steps to do implement mentioned method step 1 create your own pallet i reduced rgb color space into 216 colors of safe colors cube to step 2 cluster this pallet into some labels i clusters these 216 colors into 6 clusters classes labels and then each clusters into 6 clusters classes labels so i can label my pixels by 6 or 36 clusters classes labels are shown in the figure below each bin of 36 bins are one class of 36 classes and each batch of 6 batches are one of 6 classes step3 assign a unique value to each class and then label each pixel of your images by these values i applied both 1 6 and 1 36 labels on some images which is shown below step 4 each object will be defined by a homogeneous color layout define its region and use it some complicated and high precision methods are available you can find it by using your keywords in google and posted in cbir color pallet content based image retrieval image processing image segmentation 4 comments some useful notes about developing a complete image retrieval engine by using color and texture march 14 2008 by zahra as i promised here is some useful notes about developing a complete image retrieval engine by using color and texture 1 image gallery using a free database which have 1000 middle sized image in 10 different categories 2 feature extraction color 3 64 bin histogram in hsv color mode 64 bin histogram for each h s and v dimention texture calculating co occurrence matrix for each image and extracting contrast correlation energy and homogeneity of texture results shows that the respective importance of these feature are correlation homogeneity contrast and energy some unique blocking methods are used to extract both features in the way that the main parts of image have higher impression and importance 3 clustering i ve used k means algorithm to partition my feature space into 7 clusters respect to 7 feature vectors previously mentioned but the main criterion for decision is histogram clusters i am not satisfied by using this method so i am finding a better way to cluster my feature space i found some articles which are concerned about this issue thomas deselaers et al clustering visually similar images to improve image search engines ioan cleju et al clustering by principal curve with tree structue xin zheng et al locality preserving clustering for image databse 4 similarity analogy i ve used level 1 of minkowsky distance for histogram analogy and level 3 for texture related features for minkowsky distance formula refer to long f zhang h and dagan feng d fundamentals of content based image retrieval in multimedia information retrieval and management echnological fundamentals and applications springer verlag pp 1 26 2003 i ve used reverse of calculated distance to find the similarity rank of each images these ranks should be added together to calculate final rank of each images at this point it should not be forgotten to normalize each rank since each feature has different importance different coefficient correspond to its importance should be multiplied into its calculated rank to know how to reach a normalized rank i refer you to read this paper li x chen s c m l shyu and furht b image retrieval by color texture and spatial information in 8th international conference on distributed multimedia systems dms 2002 san francisco bay california usa 2002 pp 152 159 5 final result to find the similar images from database to a user defined image first of all fv feature vector should be extracted using the same way as other images in database since i use 256 element histogram vector to partition image databse the histogram part of fv have been used to find the respective cluster after this step the comparable images will be limited to the images belong to the respective cluster now by using similatrity measure and finding the specific ranks and them add them in that special way the similarity ranks will be assigned to every comparable images the last thing to do is to sort this rates in descending order and show n first high rank images to user note that each phase is capable to be improved posted in cbir content based image retrieval developing an image retrieval image processing 50 comments image processing open library february 18 2008 by zahra yesterday i heard something about image processing libraries there are some libraries which contains image processing main algorithms you know academic algorithms are developed using matlab or some other open source mathematical applications but they are just academic and not in purpose of business because those algorithms works very slow so to develope a bussiness class application in field of image processing some ides like visual c will be used these libraries designs for these purposes one of the most important of them is opencv which is developed by intel and is compatible with intel image processing chipset self intro of this library is ch opencv package is ch binding to opencv ith ch opencv package c or c programs using opencv c functions can readily run in ch without compilation the latest ch opencv package can be obtained from http www softintegration com products thirdparty opencv or http openvc sourceforge net ch is an embeddable c c interpreter for cross platform scripting 2d 3d plotting numerical computing and embedded scripting ch is freely available from softintegration inc http www softintegration com some other libraries will be found in http sf net posted in image processing image processing open library opencv 2 comments content based image retrieval components february 3 2008 by zahra as shown in the following figure some major components of a cbir are image database feature extraction block indexing block feature database search and retrieval block user interface user relevant feedback processing block from i search tutorial row images are stored into image database in order to fast access to these images some descriptors should be extracted from them which describe them in the best way these descriptor appear into integer or real values in order to be comparable these values called feature vectors these vectors make it easy to classify images into some predefined classes by classification methods or into non predefined clusters by clustering methods this is the duty of a block called indexing block now system is ready to accept the queries entered by user this query appears as an input image which is desired image for user actually user tells system that retrieve some images which is most similar to the quety image search and retrieval block uses send the query image to feature extraction block to extract its feature vector then uses it to search into classes clusters to find out which kernel of these classes clusters is nearest to the feature vector then some of most similar images to the query image retriev and show to user after these steps user can see the retrieved images some systems give this opportunity to user to select the images which satisfy his her more than others then this knowledge processes and affect the previus search result so that the result may be most satisfiable for user some flowchart will be added soon view an image retrieval system prototype in persian farsi فارسی language references section will be useful for all other languages note that all of them are available for free all of them are listed below continue reading posted in cbir content based image retrieval image processing 5 comments and i wondered january 2 2008 by zahra just look at this site http videolectures net i found video of a lecture about feature extraction as you see you are able to view current presenting slide beside its video who said heaven is so far away posted in image processing leave a comment content based image retrieval cbir december 9 2007 by zahra cbir is about developing an image search engine not only by using the text annotated to the image by an end user as traditional image search engines but also using the visual contents available into the images itselves initially cbir system should has a database containing several images to be searched then it should derive the feature vectors of these images and stores them into a data structure like on of the tree data structures these structures will improve searching efficiancy a cbir system gets a query from user whether an image or the specification of the desired image then it searchs the whole database in order to find the most similar images to the input or desired image the main issues in improving cbir systems are which features should be derived to describe the images better within database which data structure should be used to store the feature vectors which learning algorithms should be used in order to make the cbir wiser how to participate the user s feedback in order to improve the searching result my final thesis is about improving a cbir system by menas of learning algorithms so i will write about it in detail here i am currently working on these issues color and texture feature derivation image blocking related to the previous one color indexing posted in cbir content based image retrieval image processing image search engine 77 comments iranians fuzzi image processing december 1 2007 by zahra if you are an ai student or graduated you ve may passed the course called fuzzy logic atherwise you ve may heard about it fuzzy logic is about decision making using uncertain observations but there is certainty about the measurement of this ambiguity is it confusing don t worry this is one of million amazing descriptions of this newborn logic fuzzy logic was introduced by prof lotfali asker zadeh known as prof lotfi zadeh at berkley and continued by prof mamdani these professors are both iranian so it is said that fuzzy logic was started and ended by iranians i found this web page when i wanted to find some articles about fuzzy image processing it is the homepage of fuzzy image processing belongs to university of waterloo which takes the highest rank in google it is nice to know that here is managed by prof hamid r tizhoosh who is an iranian professor too so despite that i am not an extreme nationalist but i can conclude that iranians have conquered the top of fuzzy logic here is an abstract about fuzzy image processing to make us familiar with this issue fuzzy image processing is the collection of all approaches that understand represent and process the images their segments and features as fuzzy sets the representation and processing depend on the selected fuzzy technique and on the problem to be solved from tizhoosh fuzzy image processing springer 1997 posted in fuzzi image processing image processing iranian fuzzy logic 2 comments older posts search for archives december 2009 may 2008 april 2008 march 2008 february 2008 january 2008 december 2007 november 2007 recent comments 86emile on content based image retrieval bhanu prakash on content based image retrieval bhanu prakash on content based image retrieval saragroups on content based image retrieval asma on content based image retrieval pages about cbir applications cbir code image processing galleries useful resources meta create account log in entries feed comments feed wordpress com blog stats 48 110 hits create a free website or blog at wordpress com wpthemes image processing create a free website or blog at wordpress com subscribe subscribed image processing sign me up already have a wordpress com account log in now privacy image processing subscribe subscribed sign up log in report this content view site in reader manage subscriptions collapse this bar loading comments write a comment email required name required website design a site like this with wordpress com get started