Indexing of Content-Based Image Retrieval System with Image Understanding Approach

This paper presents a novel efficient semantic image classification algorithm for high-level feature indexing of high-dimension image database. Experiments show that the algorithm performs well. The size of the train set and the test set is 7 537 and 5 000 respectively. Based on this theory, another ground is built with 12,000 images, which are divided into three classes: city, landscape and person, the total result of the classifications is 88.92%, meanwhile, some preliminary results are presented for image understanding based on semantic image classification and low level features. The groundtruth for the experiments is built with the images from Corel database, photos and some famous face databases.

Deep Root Memory Optimized Indexing Methodology for Image Search Engines

Digitization has created an abundance of new information sources by altering how pictures are captured.Accessing large image databases from a web portal requires an opted indexing structure instead of reducing the contents of different kinds of databases for quick processing.This approach paves a path toward the increase of efficient image retrieval techniques and numerous research in image indexing involving large image datasets.Image retrieval usually encounters difficulties like a)merging the diverse representations of images and their Indexing,b)the low-level visual characters and semantic characters associated with an image are indirectly proportional,and c)noisy and less accurate extraction of image information(semantic and predicted attributes).This work clearly focuses and takes the base of reverse engineering and de-normalizing concept by evaluating how data can be stored effectively.Thus,retrieval becomes straightforward and rapid.This research also deals with deep root indexing with a multidimensional approach about how images can be indexed and provides improved results in terms of good performance in query processing and the reduction of maintenance and storage cost.We focus on the schema design on a non-clustered index solution,especially cover queries.This schema provides a filter predication to make an index with a particular content of rows and an index table called filtered indexing.Finally,we include non-key columns in addition to the key columns.Experiments on two image data sets‘with and without’filtered indexing show low query cost.We compare efficiency as regards accuracy in mean average precision to measure the accuracy of retrieval with the developed coherent semantic indexing.The results show that retrieval by using deep root indexing is simple and fast.

A Novel Adaptive Approach to Process Binary Fingerprint Images Using Directional Morphological Operations

An adaptive algorithm for removing false ridges, bridges and filling gaps in binary fingerprint images based on morphological operations is presented. A novel procedure for structuring elements design based on the specific fingerprint characteristic is described. Using the images from FVC2000 database, we have compared our method proposed here with the approach proposed by other ones. The Experimental results have demonstrated the efficiency of our method.

Efficient Cloud Image Retrieval System Using Weighted-Inverted Index and Database Filtering Algorithms

With the advance of multimedia technology and communications, images and videos become the major streaming information through the Internet. How to fast retrieve desired similar images precisely from the Internet scale image/video databases is the most important retrieval control target. In this paper, a cloud based content-based image retrieval （CBIR） scheme is presented. Database-categorizing based on weighted-inverted index （DCWII） and database f＇dtering algorithm （DFA） is used to speed up the features matching process. In the DCWII, the weights are assigned to discrete cosine transform （DCT） coefficients histograms and the database is categorized by weighted features. In addition, the DFA filters out the irrelevant image in the database to reduce unnecessary computation loading for features matching. Experiments show that the proposed CBIR scheme outperforms previous work in the precision-recall performance and maintains mean average precision （mAP） about 0.678 in the large-scale database comprising one million images. Our scheme also can reduce about 50% to 85% retrieval time by pre-filtering the database, which helps to improve the efficiency of retrieval systems.

Denoising Letter Images from Scanned Invoices Using Stacked Autoencoders

Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.

Simulation and Experimental Method for Microwave Oven

The simulation software, HFSS （high frequency structure simulator）, is introduced in microwave oven design. In the cold test, a network analyzer is used to measure the reflection coefficient （S11） of the cavity under empty and loaded states over the frequency range from 2.448 GHz to 2.468 GHz. In the hot test, a piece of wet thermal paper and an infrared thermal imaging camera are used to measure the electric field distributions on the mica and turntable. In the cold test, the simulation agrees well with the experiment no matter in empty state or loaded state. In the hot test, the simulation agrees well with the experiment in general in empty state and approximately in loaded state. The little difference in both cold and hot test may be due to that the model in simulation is not absolutely identical with that in experiment or the inadequate precision of infrared thermal imaging camera.

COLOR FLOW IMAGING OF HEPATIC CARCINOMA

Color flow imaging(CFI)ultrasound technique can discover the tumor vascularity and superimposed it to convontional B-mode ultrasonogram in real-time. The flow velocity on spectral Doppler sonogram can be measured by setting the sample volume to any selected site.One hundred and thirty-six patients with solid hepatic space-occupied lesions had been admitted and 113 cases were confirmed by operation and pathology,23 patients were strongly suspected by hepatic angiography (HAA).Ninety nine patients with 109 nodules were finally diagnosed as hepatic cellular carcinoma(HCC).According to color flow distribution pattern,3 kinds of color configuation had been nominated.Tumor vascularity discovered by CFI,especially the arterial blood flow was easy to be recognized,and its emerge rate was quite different between HCC group(94.5%)and hemangioma(HCH)group(17.07%)(P<0.01).Spectral Doppler studies were also carried out in these cases and the detectability of arterial flow in HCC group(95.41%)was much higher than that to HCH group (21. 95%) (P<0. 005).Resistant index(RI)and pulsatile index(PI)could be used to differentiate HCC (>0. 50 and >0.80 respectively)from HCH (P<0.001 and P<0.001 respectively).Arterial-portal(A-P) shunt could also be detected by CFI and spectral Doppler(mostly its Vmax>0.6m/s).The detection rate of A-P shunt was 64% in HCC group,but no case could be detected in HCH group.

Experimental far-field imaging properties of high refractive index microsphere lens

The far-field imaging properties of a high index microsphere lens spatially separated from the object are experimentally studied. Our experimental results show that, for a Blu-ray disk whose spacing is 300 nm, the high index microsphere lens also can discern the patterns of the object sample when the distance between the lens and the object is up to 5.4 μm. When the distance is increased from 0 to 5.4 μm, for the microsphere lens with a diameter of 24 μm, the lateral magnification increases from 3.5× to 5.5×, while the field of view decreases from 5.1 to 3.0 μm. By varying the distance between the lens and the object, the optical image can be optimized. We also indicate that the far-field imaging capability of a high index microsphere lens is dependent on the electromagnetic field intensityprofile of the photonic nanojet under different positions of the microsphere lens.