SIMPLE Descriptors (Extended) 简单图像描述子

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SIMPLE [Searching Images with Mpeg-7 (& Mpeg-7 like) Powered Localized dEscriptors] begun as a collection of four descriptors [Simple-SCD, Simple-CLD, Simple-EHD and Simple-CEDD (or LoCATe)]. The main idea behind SIMPLE is to utilize global descriptors as local ones. To do this, the SURF detector is employed to define regions-of-interest on an image, and instead of using the SURF descriptor, one of the MPEG-7 SCD, the MPEG-7 CLD, the MPEG-7 EHD and the CEDD descriptors is utilized to extract the features of those image’s patches. Finally, the Bag-Of-Visual-Words framework is used to test the performance of those descriptors in CBIR tasks. Furthermore, recently SIMPLE was extended from a collection of descriptors, to a scheme (as a combination of a detector and a global descriptor). Tests have been carried out after utilizing other detectors [the SIFT detector and two Random Image Patches’ Generators (The Random Generator has produced the best results and is portrayed as the preferred choice.)] and currently the performance of that scheme with more global descriptors is being tested. From this page, one can download the open source implementation of the SIMPLE descriptors (C#, Java and MATLAB)

Searching Images with MPEG-7 (& MPEG-7 Like) Powered Localized dEscriptors (SIMPLE)
A set of local image descriptos specifically designed for image retrieval tasks

Image retrieval problems were first confronted with algorithms that tried to extract the visual properties of a depiction in a global manner, following the human instinct of evaluating an image’s content. Experimenting with retrieval systems and evaluating their results, especially on verbose images and images where objects appear with partial occlusions, showed that the accepted correctly ranked results are positively evaluated by the extraction of the salient regions of an image, rather than the overall depiction. Thus, a representation of the image by its points of interest proved to be a more robust solution. SIMPLE descriptors, emphasize and incorporate the characteristics that allow a more abstract but retrieval friendly description of the image’s salient patches.

List of SIMPLE DescriptorsDetectorDescriptorAcronymSURFColor and Edge Directivity Descriptor – CEDDSIMPLE srf-CEDD OR LoCATeSURFMPEG-7 Scalable Color Descriptor – SCDSIMPLE srf-SCSURFMPEG-7 Color Layout Descriptor – CLDSIMPLE srf-CLSURFMPEG-7 Edge Histogram – EHSIMPLE srf-EHSIFTColor and Edge Directivity Descriptor – CEDDSIMPLE sft-CEDDSIFTMPEG-7 Scalable Color Descriptor – SCDSIMPLE sft-SCSIFTMPEG-7 Color Layout Descriptor – CLDSIMPLE sft-CLSIFTMPEG-7 Edge Histogram – EHSIMPLE sft-EHRandomColor and Edge Directivity Descriptor – CEDDSIMPLE rnd-CEDDRandomMPEG-7 Scalable Color Descriptor – SCDSIMPLE rnd-SCRandomMPEG-7 Color Layout Descriptor – CLDSIMPLE rnd-CLRandomMPEG-7 Edge Histogram – EHSIMPLE rnd-EHGaussRandomColor and Edge Directivity Descriptor – CEDDSIMPLE gaussRnd-CEDDGaussRandomMPEG-7 Scalable Color Descriptor – SCDSIMPLE gaussRnd-SCGaussRandomMPEG-7 Color Layout Descriptor – CLDSIMPLE gaussRnd-CLGaussRandomMPEG-7 Edge Histogram – EHSIMPLE gaussRnd-EH

Experiments were contacted on two well-known benchmarking databases. Initially experiments were performed using the UKBench database. The UKBench image database consists of 10200 images, separated in 2250 groups of four images each. Each group includes images of a single object captured from different viewpoints and lighting conditions. The first image of every object is used as a query image. In order to evaluate our approach, the first 250 query images were selected. The searching procedure was executed throughout the 10200 images. Since each ground truth includes only four images, the P@4 evaluation method to evaluate the early positions was used.

In the sequel, experiments were performed using the UCID database. This database consists of 1338 images on a variety of topics including natural scenes and man-made objects, both indoors and outdoors. All the UCID images were subjected to manual relevance assessments against 262 selected images.

In the tables that illustrate the results, wherever the BOVW model is employed, only the best result achieved by each descriptor with every codebook size, is presented. In other words, for each local feature and for each codebook size, the experiment was repeated for all 8 weighting schemes but only the best result is listed in the tables. Next to the result, the weighting scheme for which the result was achieved is noted (using the System for the Mechanical Analysis and Retrieval of Text – SMART notation)

Experimental Results of all 16 SIMPLE descriptors on the UKBench and the UCID dataset. MAP results in bold fonts mark performances that surpass the baseline performance. Grey shaded results mark the highest performance achieved per detector

Downloads

SIMPLE Descriptors with Random Image Patches’ Generator (Suggested Download) – C# Source Code and DLL [Download] (GNU GPL).

This is the easiest way to incorporate in your application the SIMPLE descriptors (no external dependencies). Simply add the dll as reference in your c# project and then call the classes as:

* The Number 600 refers to the number of samples per image.

For the other SIMPLE Descriptors simply select another method from the SIMPLE class

Download an example application, in which we describe the retrieval procedure using the above DLL. Source Code included – C# Source Code [Download] (GNU GPL)

SIMPLE Descriptors with SIFT and SURF Detector as Patches’ Generator (64) – C# Source Code and DLL [Download] (GNU GPL)
SIMPLE Descriptors with SIFT and SURF Detector as Patches’ Generator (32)- C# Source Code and DLL [Download] (GNU GPL)
SIMPLE Descriptors with Gaussian Random Image Patches’ Generator – C# Source Code and DLL [Download] (GNU GPL)
Download an example application, in which we describe the retrieval procedure. Source Code included – C# Source Code [Download] (GNU GPL)
Third party tutorial (Special thanks to SHABBIR AKOLAWALA)

SIMPLE-Locate Descriptor with SURF – MATLAB Source Code [Download] (FAPO*) The source code is quite simple and easy to be handled by all users. There is a main function that has the task of extracting the LoCATe descriptor from a given image

[*] For academic purposes only

A JAVA implementation of the descriptors is available through the LIRE library [Updated]. Lire is an open source project hosted at Google Code

For questions, comments, suggestions for improving or bugs reporting please send an email to Nektarios Anagnostopoulos [nek.anag<at>gmail<dot>com]

Details regarding these descriptors can be found at the following paper: (in other words, if you use these descriptors in your scientific work, we kindly ask you to cite one of the following papers )

C. Iakovidou, N. Anagnostopoulos, A. Kapoutsis, Y. Boutalis, M. Lux and S. A. Chatzichristofis, “LOCALIZING GLOBAL DESCRIPTORS FOR CONTENT BASED IMAGE RETRIEVAL. SIMPLIFYING THE SIMPLE FAMILY OF DESCRIPTORS”, «EURASIP Journal on Advances in Signal Processing», Springer, Volume 2015, Issue 80, 7 September 2015, pp 1-20.

C. Iakovidou, N. Anagnostopoulos, A. Ch. Kapoutsis, Y. Boutalis and S. A. Chatzichristofis, “SEARCHING IMAGES WITH MPEG-7 (& MPEG-7 LIKE) POWERED LOCALIZED DESCRIPTORS: THE SIMPLE ANSWER TO EFFECTIVE CONTENT BASED IMAGE RETRIEVAL”, «12th International Content Based Multimedia Indexing Workshop», June 18-20 2014, Klagenfurt – Austria. [Download the paper] [Download the Presentation]

Additional Staff:

Slides from the presentation of the SIMPLE descriptors @ CBMI2014:

Photos from the presentation of the SIMPLE descriptors @ CBMI2014: