Ontology supported semantic based image retrieval

1. the sentence added in the abstract to report the stats should be written a the end of the abstract not a the beginning.
2. The authors must explain the variables in Equations 1 t0 4 below the equations to enable the reader to understand these equations
3. The paper must be proof read by a native English speaking person.
4. Table 1 does not have a title; the authors have explained the Table in lieu of the title; the explanation must be moved and a title provided.

The authors have resolved most of my comments and the paper has been improved. It can be accepted.

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Summary:

The paper proposes a novel two-stage Semantic-Based Image Retrieval (SBIR) system supported by Ontology, aiming to address the Semantic Gap in traditional Image Retrieval approaches. In the first stage, Object Detection and a Bi-directional Recurrent Neural Network (Bi-RNN) model are employed to determine relationships between objects. The second stage involves converting these relationships into Ontologies for more effective semantic similarity searches. The study introduces a method for measuring Semantic Gap using entropy.

Contributions:

Novel Ontology-Supported Approach: The paper introduces a unique two-stage SBIR approach that utilizes Ontologies for improved semantic representation and retrieval.

Effective Use of Visual Genome Dataset: The study employs the Visual Genome dataset for training the model and generating ontologies, contributing to the credibility of the research.

Innovative Semantic Gap Measurement: The use of entropy, specifically the Joint Entropy method, for measuring the Semantic Gap between images adds a novel quantitative dimension to the evaluation.

Strengths:
Originality:
The paper demonstrates a high level of originality by combining Object Detection, Bi-RNN models, and Ontology in a novel two-stage SBIR approach. The introduction of entropy for Semantic Gap measurement further contributes to the originality of the study.

Significance of Results:
The proposed approach, especially the second stage utilizing Ontologies, is shown to yield more effective results in semantic similarity searches compared to existing methods. The introduction of a quantitative measure for Semantic Gap provides a valuable contribution to the field.

Quality of Writing:
The writing is generally clear and concise, with well-organized sections. The technical details, such as the Word Embedding process, Bi-RNN model description, and Ontology creation, are presented in a detailed manner.

Comments-

Weak points have been addressed in the revised version, manuscript can be considered for acceptance.