Content-Based Image Retrieval (CBIR) By: Victor Makarenkov Michael Marcovich Noam Shemesh

What is CBIR Content-based image retrieval, a technique which uses visual contents to search images from large scale image databases according to users' interests, has been an active and fast advancing research area since the 1990s. In our project we concentrated on region- histogram features to retrieve the images according to an example query image supplied by the user.

Histogram is a measure used to describe the image. In simple words it means the distribution of color brightness across the image. The brightness values range in [0..255]. Region based means that the histogram measure is not taken globally for the whole image, but locally for different image regions. This region-histogram features were used as index of the image database. Weighting for the regions. The closer the part to image center – the higher its weight in similarity measure.

General CBIR system works according to the following schema : In our CBIR system we implemented all the parts except the one of relevance feedback.

Visual content description: since we using histogram of image, we transform the file of the image to its bitmap representation. That means 2D array where each cell contains a triple with the RGB brightness values for the colors Red, Green, Blue.

Feature Vector  Feature vectors: In our system, for generality purposes we assume that the images are of fixed size 200*200 pixels. (If not our system converts them to that size). We use local histogram values. The image is divided into N * N square areas, and then the histogram computed in each area.Each area is of size (200/N)*(200/N) pixels.Each image is represented with N*N length vector where each coordinate is the histogram in the appropriate area. More precisely: and.

Similarity comparison: for a similarity comparison we used the Minkowski distance. Minkowski distance between 2 images I and J is denoted as: while we started our research when p=2. Indexing and retrieval: for all images that are in the databases the feature vector is pre-computed and stored as index in file. When retrieval should be made, the image with the least Minkowski (most similar images) distance between query image and image from database is returned.

Conclusions: As we thought at the beginning – Histogram is quite primitive and insufficient way for CBIR purposes. However, with certain image characteristics it may be useful, and works well. For example on the military ceremony and the nature images. Another important foundation we made is that one of our initial assumptions was wrong. It is that dividing the image into many area, does not always improve the results of retrieval. In case of too many divisions, it degrades the results. The reason for that is that while comparing small parts, that are corresponding between the images and are at fixed place, they can be different. But if the same picture can be shifted, and not be found! The method is not shift invariant! In some cases, small division (4 areas) did help. For example on the image of Barcelona it moved a similar shifted building up 1 in rank. The "Minkowski distance" that was changed several times during our experiments did not make dramatic changes, but moved some further images close when P is enlarged. May be used to tuning when similar content image exists, but is not ranked top. Enlarging P in that case can "push" its rank higher.

References: –Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng. Content Based Image Retrieval. –Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng. An Effective Region-Based Image Retrieval Framework –Yossi Rubner, Carlo Tomasi, and Leonidas J. Guibas The Earth Mover's Distance as a Metric for Image Retrieval