الفهرس | Only 14 pages are availabe for public view |
Abstract Information retrieval (IR) is the science of searching for information, which has several forms such as text retrieval, content-based image retrieval (CBIR), and text-to-image retrieval. Any form of IR, or any feature-based application, mainly depends on the content- based similarity matching process. For CBIR, it is a standard procedure that measures the spatial distance between the derived features from a query image and others derived from the set of training images. It concerns the retrieval of visual information from databases using image comparison to detect related images in datasets. It is worthy of note that ”image representation” is the key engine behind the entire process. This is guaranteed across all proposed test cases for performance evaluation for image matching and retrieval. However, image representation and consequently similarity matching face several challenges (hurdles), especially for CBIR. At first, the quantity of detected features and their density must be compatible with the image content. However, in terms of retrieval accuracy and redundancy, the number of detected features is scarcely impacted by the quality or sort of the utilised images. In such cases, redundancy affects not only the size of the applied images but also the quantum of their extracted visual features. Generated descriptor ”dimensionality” by each feature extraction method hardly affects matching and retrieval in terms of speed and used memory. Thus, there is a trade-off between the quality of the applied image and the quantity of detected features or retrieval accuracy compared to the speed of retrieval. Also, features extracted are hardly affected by image rotation, shifting, flipping, noise, affine distortion, and others. With the massive growth of web images, the scale of databases is enlarged, and new hurdles are added to the previously mentioned. At first, a suitable deep learning method (CNN or other) is hardly required to handle large-scale datasets in a general fashion across different image sorts. In addition, redundancy has an impact across large-scale datasets. Labelling and indexing are effective, as response time is a key issue in retrieval. For efficient image representation, especially for sketches, segregate object features as a separate variable across a large-scale dataset. Also, it is required to avoid recursion because of the local correlation between pixels. It is highly required to allow model to focus on specific parts of input by assigning different weights to certain parts of input. Hence, for swift and accurate outcomes, efficient feature representation of compared images has a major influence on the matching |