The need for efficient content-based image retrieval has increased tremendously in many application areas such as biomedicine, military, commerce, education, and web image classification and searching. Currently, rapid and effective searching for desired images from large-scale image database becomes an important and challenging research topic. Content - based Image Retrieval (CBIR) technology overcomes the defects of traditional text-based image retrieval technology, such as heavy workload and strong subjectivity.
It makes full use of image content features (color, texture, shape, etc.), which are analyzed and extracted automatically by computer to achieve the effective retrieval Using a single feature for image retrieval cannot be a good solution for the accuracy and efficiency. High dimensional feature will reduce the query efficiency; low-dimensional feature will reduce query accuracy, so it may be a better way using multi features for image retrieval. Color and texture are the most important visual features. Firstly, we discuss the color and texture features separately.
On this basis, a new method using integrated features is provided, and experiment is done on the real images, satisfactory result is achieved, verify the superiority of integrated feature than the single feature.
This paper studies the visual feature extraction of image retrieval. According to HSV color space, we quantify the color space in non-equal intervals, construct one-dimensional feature vector and represent the color feature by cumulative histogram.
In describing the image texture features, we use the gray-level co-occurrence matrix (GLCM) and Gabor wavelets respectively. Finally, the HSV color features are combined with GLCM and Gabor wavelets respectively for image retrieval. Experiment results show the effectiveness of the algorithm
The ADSP-BF533/32/31 processors are enhanced members of the Blackfin processor family that offer significantly higher performance and lower power than previous Blackfin processors while retaining their ease-of-use and code compatibility benefits, processors are completely code and pin-compatible, differing only with respect to their performance and on-chip memory.
The Blackfin processor core architecture combines a dual MAC signal processing engine, an orthogonal RISC-like microprocessor instruction set, flexible Single Instruction, Multiple Data (SIMD) capabilities, and multimedia features into a single instruction set architecture. Blackfin products feature dynamic power management. The ability to vary both the voltage and frequency of operation optimizes the power consumption profile to the specific task.
The BLACKFIN EVALUATION BOARD is specially designed for developers in dsp field as well as beginners. The BF532 kit is designed in such way that all the possible features of the DSP will be easily used by everyone. The kit supports in VisualDsp++5.0 and later.
The Blackfin processor core contains two 16-bit multipliers, two 40-bit accumulators, two 40-bit ALUs, four video ALUs, and a 40-bit shifter. The computation units process 8-bit, 16-bit, or 32-bit data from the register file. The compute register file contains eight 32-bit registers. When performing compute operations on 16-bit operand data, the register file operates as 16 independent 16-bit registers. All operands for compute operations come from the multiported register file and instruction constant fields.
Each MAC can perform a 16-bit by 16-bit multiply in each cycle, accumulating the results into the 40-bit accumulators. Signed and unsigned formats, rounding, and saturation are supported.
The ALUs perform a traditional set of arithmetic and logical operations on 16-bit or 32-bit data. In addition, many special instructions are included to accelerate various signal processing tasks. These include bit operations such as field extract and population count, modulo 232 multiply, divide primitives, saturation and rounding, and sign/exponent detection.
The set of video instructions includes byte alignment and packing operations, 16-bit and 8-bit adds with clipping, 8-bit average operations, and 8-bit subtract/absolute value/accumulate (SAA) operations. Also provided are the compare/select and vector search instructions.
The 40-bit shifter can perform shifts and rotates and is used to support normalization, field extract, and field deposit instructions.
The program sequencer controls the flow of instruction execution, including instruction alignment and decoding. For program flow control, the sequencer supports PC relative and indirect conditional jumps (with static branch prediction), and subroutine calls. Hardware is provided to support zero-overhead looping. The architecture is fully interlocked, meaning that the programmer need not manage the pipeline when executing instructions with data dependencies.
Blackfin processors support a modified Harvard architecture in combination with a hierarchical memory structure. Level 1 (L1) memories are those that typically operate at the full processor speed with little or no latency. At the L1 level, the instruction memory holds instructions only.
An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords, or descriptions to the images so that retrieval can be performed over the annotation words.
Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools.
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. (see this survey for a recent scientific overview of the CBIR field). Content based image retrieval is opposed to concept based approaches (see concept based image indexing).
"Content-based" means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image.
The term 'content' in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because most web based image search engines rely purely on metadata and this produces a lot of garbage in the results.
Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image. Thus a system that can filter images based on their content would provide better indexing and return more accurate results.
The following are playing a major in our project:
The Blackfin Evaluation Board has 128Mbit SDRAM interfaced in BF532 kit. This interface will use to store a huge data (pixel) .
The RS232 9 pin serial communication is interfaced through UART Serial Interface peripheral. This interface is use to communicate kit with the Matlab.
The Visual Dsp++ will help us to do the source code for Blackfin 532 to implement the Image Retrieval algorithm and to debug.
The MatLab R2010a will help us to see images on GUI Window from processor uart through pc.
This Figure shows the implementation of Image Retrieval algorithm in this project
The program has written as per the above explanation. This project you can implement in directly to BF532 kit. This programming concept you can use any one of the BF533/32/31 Blackfin processor with good SDRAM capacity to handle huge datas, such a general concept implemented.
This project source code is available at our website. User can download that project once you registered. For more queries, please contact through forum.
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