An Efficient Deep Learning-based Content-based Image Retrieval Framework

Abstract

The use of massive image databases has increased drastically over the few years due to evolution of multimedia technology. Image retrieval has become one of the vital tools in image processing applications. Content-Based Image Retrieval (CBIR) has been widely used in varied applications. But, the results produced by the usage of a single image feature are not satisfactory. So, multiple image features are used very often for attaining better results. But, fast and effective searching for relevant images from a database becomes a challenging task. In the previous existing system, the CBIR has used the combined feature extraction technique using color auto-correlogram, Rotation-Invariant Uniform Local Binary Patterns (RULBP) and local energy. However, the existing system does not provide significant results in terms of recall and precision. Also, the computational complexity is higher for the existing CBIR systems. In order to handle the above mentioned issues, the Gray Level Co-occurrence Matrix (GLCM) with Deep Learning based Enhanced Convolution Neural Network (DLECNN) is proposed in this work. The proposed system framework includes noise reduction using histogram equalization, feature extraction using GLCM, similarity matching computation using Hierarchal and Fuzzy c-Means (HFCM) algorithm and the image retrieval using DLECNN algorithm. The histogram equalization has been used for computing the image enhancement. This enhanced image has a uniform histogram. Then, the GLCM method has been used to extract the features such as shape, texture, colour, annotations and keywords. The HFCM similarity measure is used for computing the query image vector's similarity index with every database images. For enhancing the performance of this image retrieval approach, the DLECNN algorithm is proposed to retrieve more accurate features of the image. The proposed GLCM+DLECNN algorithm provides better results associated with high accuracy, precision, recall, f-measure and lesser complexity. From the experimental results, it is clearly observed that the proposed system provides efficient image retrieval for the given query image.

Unveiling Visual Treasures: Harnessing Deep Learning for Content-Based Image Retrieval

“…The ILL-SMO (25) issue, despite its complexity, faces challenges like low search accuracy, slow convergence time, and local optimality, requiring complex and time-consuming parameter selection. RULBP (26) promotes rotation invariance; its accuracy may not increase with increasing computing complexity due to binary pattern clarity, potentially causing inaccurate representations of fundamental visual data. GLCM-DLECNN (26) , despite its computational complexity and accuracy issues, has the potential to enhance texture-based image retrieval, especially for color images with larger dimensionality.…”

“…RULBP (26) promotes rotation invariance; its accuracy may not increase with increasing computing complexity due to binary pattern clarity, potentially causing inaccurate representations of fundamental visual data. GLCM-DLECNN (26) , despite its computational complexity and accuracy issues, has the potential to enhance texture-based image retrieval, especially for color images with larger dimensionality. Its efficiency can be impacted by preprocessing biases.…”

Locality-Sensitive Hashing of Soft Biometrics for Efficient Face Image Database Search and Retrieval

“…Hence, there has been a sharp increase in the use of enormous image databases over the past few years. The emergence of content-based image retrieval (CBIR) methods in the research community has been inspired by the prevalence of big multimedia data [4,5]. Image search and retrieval have recently attracted much attention because of the increasing necessity to extend their capabilities and efficacy to be better suited for handling large-scale databases, which may include billions of samples.…”

Fusion of Hash-Based Hard and Soft Biometrics for Enhancing Face Image Database Search and Retrieval

“…Every day, a massive volume of multimedia data, including texts, photos, and videos, is created on the internet due to social media and search engines' revolutionary growth. Therefore, there was a sharp concurrent rise in utilizing large photo databases [1,2]. Recently, content-based image retrieval (CBIR) has drawn a lot of attention due to the growing need to enhance the efficiency and capacities of picture search and retrieval to better handle large-scale databases that may contain billions of images.…”