The Image search by content is an area that is based on a set of low-level features such as histograms, textures, the distribution of colors, shapes an brightness. The structure element is a shortcut of the image may vary depending on the designer of the XML document and which may change according to application needs. The visual appearance of the image is a permanent factor that undergoes no change. Therefore, we present in this paper a new method of search by content based on Harris detector, Haar wavelet and Color histogram for the step of feature extraction to extract a binary signature for all multimedia documents.Finally We estimate the similarities between codes and search relevant images with Hamming distance.
The research of the image content was the subject of numerous studies in recent years. Techniques developed aim to extract and represent effectively the content of images. The image indexing can be done either on the basis of low-level features or those of semantics.
The need for methods of indexing and retrieval directly based on the content of
the image is no longer evident. The first prototype system has been proposed
in 1970 and this system has attracted the attention of many researchers. Some
systems become commercial systems such as QBIC (Query By Image Content)
IBM [
The Frip (Finding Regions in the Pictures) system [
Sometimes the user wants to query such as ”Find all the images of the database
with certain regions similar to those of the query image” [
Some work has proceeded to minimize the search space using the technique of
calculating the nearest neighbors to group similar data in classes [
One solution to this problem is the technique of vectorization, which allows to
find relevant images to a query and which are not returned by the initial system.
Its sequence requires the choice of a set of said reference material. The choice of
these references is still problematic for the construction of the vector space.
In this context, it is necessary to select a heterogeneous set of documents. The
number of references is also problematic. Indeed, the number of documents is the
new dimension of vector space. If we choose a small set of documents, we may
not be able to properly represent all documents. These references are randomly
selected by [
In this section, we propose a description of the proposed visual system. In fact, the search for multimedia documents requires a critical step in the abstract offline indexing. This step requires the feature extraction from the image to give a general description of documents. Following this course indexing we can go in search of the visual image based on the descriptors already studied. Figure 1 describes the steps mentioned by an explanatory diagram. 2.1
The objective of extracting descriptors is the set up the search method contained
in the images. One example is the search for a database containing images of
plants mentioned. Images are indexed from the local descriptors such as Harris
detector [
Color Image
Feature extraction Harris detector
Haar wavelet decomposition
Color histogram Principal component analysis Extraction of binary signature 0101010101000101010101001 textures and the calculation of the histogram color to represent the distribution of intensities.
Harris detector For twenty years, several detectors points of interest have
been developed. Schmid has compared the performance of several of them [
of the sinusoid replaces the Fourier transform by a family of translations and dilations of a single function. The translation parameters and expansion of the two arguments are the wavelet transform.
Haar transform was introduced in 1910 and is the oldest wavelet transform. It is based on the demonstration of Haar (equation 1). (1) h(j, k; x) =
2j/2 2H(2jx
k) −
With H(x) = 1 f or x in ]0, 1/2[, H(x) = 1 f or x in ]1/2, 1[ and H(x) = 0 ailleurs form a complete orthogonal set for s−pace L2(R). The Haar wavelet is simple to study and implement. Figure 3 shows the result of decomposition of an image texture to analyze and extract the diagonal details, the vertical details, the horizontal details and finally the description.
Image analysis by color histogram For a monochrome image, the histogram is defined as a discrete function that maps each intensity value the number of pixels taking this value. The determination of the histogram is made by counting the number of intensity for each pixel of the image. The quantification, which includes several intensity values in one class can better visualize the distribution of image intensities.
Our job is to build a histogram of a color image in the RGB space, the red, green and blue. Each component is quantized into eight intervals. In each interval of each Red, green and blue component; we calculate the number of pixels normalized frequency. We can then represent the image by a color histogram as shown in Figure 4 following. 2.2
The principal component analysis ( P CA), developed in France in the 1960s by JP.Benzcri is an exploratory statistical method used to describe a wide array of data type individuals / variables. When individuals are described with 5 large numbers of variables, no simple graphical representation allows to visualize the point cloud formed by the data.
The PCA provides a representation in a space of reduced size, thereby allowing to highlight any structures in the data. For this, we look for subspaces where the projection of the cloud deforms the least possible initial cloud. Applying the P CA to the descriptors, previously computed, A signal f (x), that includes all the characteristic values, is obtained. This signal is then subjected to an automatic thresholding (equation 2 ) to the transformed into a binary signature C(x) (equation 3).
m Mf = X f (x) n=1 (2)
In this way each image is represented by a binary code which is stored in a database for later use in the research phase.
Once the signatures are extracted, we must think of a technique that
quantifies the distance between them, in order to identify similar images. The bit-wise
comparison of each two image codes A and B each detail is given by the
normalization of the Hamming distance (HD) [
is the boolean operator ( XOR) described by the equation 5 follows The probability that two bits of arbitrary code images are equal or not, worth P = 0.5. When the majority of codes A(j) and B(j) are equal , is therefore in the case where the two codes compared are equivalent. In the opposite case, the two codes are different. Generally, when HD tends to zero it is called relevant images.
Our group submitted just one run in your rst participation in the ImageCLEF Plant task 2014. In this paper we described a plant species retrieval model based on a extraction of unique signature for each image in collection. This signature is composed by Haar and Harris features.
(a) (3) (4) (5)
The metric evaluation S was related to the rank of the correct species in the list
of retrieved species as follows [
S = 1 XU 1 XPu 1 NXu,p Su,p,n U u=1 Pu p=1 Nu,p n=1 (6) Where U :is the number of users.
Pu : number of individual plants observed by the uth user.
Nu,p : number of pictures taken from the pth plant observed by the uth user. Su,p,n : score between 1 and 0 equals to the inverse of the rank of the correct species (for the nth picture taken from the pth plant observed by the uth user). 3.2
IVprocessing team has submitted a single automatic run to the multi-image plant observation queries. In this run, we extracted visual descriptors from images, used texture, color and interest points model for generating the feature vectors. The result is showed in Table 1. At a closer look, the score values of our results are rather low. We cannot fully explain the failure with these plant image as there were no indications for the weak performance during the training stage. One reasonable interpretation of the results is that the conducted learning runs led to an over-fitting.
A total of 10 participating groups submitted 27 runs focusing on plant observations. 6 teams submitted 14 complementary runs on images in ImageCLEF 2014. The following graphic shows the scores obtained on the main task on multi-image plant observation queries. We presented in this paper a research method by which the content is based on extracting a signature for a given image. This signature is composed by a set of descriptors, such as the analysis of the histogram of the color image, the extraction of the interest points and Haar wavelet. This signature undergoes a step dimensionality reduction by PCA method. An experimental study on ImageCLEF 2014 showed the effectiveness of our descriptors.